US20150331908A1 - Visual interactive search - Google Patents

Visual interactive search Download PDF

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US20150331908A1
US20150331908A1 US14/494,364 US201414494364A US2015331908A1 US 20150331908 A1 US20150331908 A1 US 20150331908A1 US 201414494364 A US201414494364 A US 201414494364A US 2015331908 A1 US2015331908 A1 US 2015331908A1
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documents
candidate
space
user
identifying
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Nigel Duffy
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Evolv Technology Solutions Inc
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Sentient Technologies Barbados Ltd
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Priority to US14/494,364 priority Critical patent/US20150331908A1/en
Assigned to GENETIC FINANCE (BARBADOS) LIMITED reassignment GENETIC FINANCE (BARBADOS) LIMITED ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: DUFFY, NIGEL
Assigned to SENTIENT TECHNOLOGIES (BARBADOS) LIMITED reassignment SENTIENT TECHNOLOGIES (BARBADOS) LIMITED CHANGE OF NAME (SEE DOCUMENT FOR DETAILS). Assignors: GENETIC FINANCE (BARBADOS) LIMITED
Priority to GB1621341.5A priority patent/GB2544660A/en
Priority to DE112015002286.4T priority patent/DE112015002286T9/de
Priority to JP2016567798A priority patent/JP2017518570A/ja
Priority to PCT/IB2015/001267 priority patent/WO2015173647A1/fr
Priority to TW104114144A priority patent/TW201606537A/zh
Priority to US15/311,163 priority patent/US10503765B2/en
Priority to EP15760512.2A priority patent/EP3143523B1/fr
Priority to CN201580038513.4A priority patent/CN107209762B/zh
Publication of US20150331908A1 publication Critical patent/US20150331908A1/en
Priority to US15/295,930 priority patent/US10606883B2/en
Priority to US15/295,926 priority patent/US20170039198A1/en
Priority to US15/373,897 priority patent/US10102277B2/en
Priority to US16/681,514 priority patent/US11216496B2/en
Assigned to EVOLV TECHNOLOGY SOLUTIONS, INC. reassignment EVOLV TECHNOLOGY SOLUTIONS, INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: SENTIENT TECHNOLOGIES HOLDINGS LIMITED
Assigned to SENTIENT TECHNOLOGIES HOLDINGS LIMITED reassignment SENTIENT TECHNOLOGIES HOLDINGS LIMITED ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: SENTIENT TECHNOLOGIES (BARBADOS) LIMITED
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    • G06F17/30477—
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/33—Querying
    • G06F16/332—Query formulation
    • G06F16/3325—Reformulation based on results of preceding query
    • G06F16/3326—Reformulation based on results of preceding query using relevance feedback from the user, e.g. relevance feedback on documents, documents sets, document terms or passages
    • G06F16/3328—Reformulation based on results of preceding query using relevance feedback from the user, e.g. relevance feedback on documents, documents sets, document terms or passages using graphical result space presentation or visualisation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/904—Browsing; Visualisation therefor
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2455—Query execution
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/93—Document management systems
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/957—Browsing optimisation, e.g. caching or content distillation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/25—Integrating or interfacing systems involving database management systems

Definitions

  • the invention relates generally to a tool for searching for digital documents in an interactive and visual way.
  • digital documents include: photographs, product descriptions, or webpages.
  • this tool may be used on a mobile device to search for furniture available for sale via an online retailer.
  • the queries may be in the form of a structured query language, natural language text, speech, or a reference image.
  • the results returned often do not satisfy the user's search goal. The user then proceeds to refine or modify the query in an attempt to better achieve desired goals.
  • the system described herein addresses this challenge in at least two ways. First, it presents a set of search results that are diverse along multiple axes. Second, it allows the user to interactively “navigate” within those results by selecting results that incrementally address desired search goals and using that user input to iteratively refine the set of results presented to the user.
  • a user performs an initial search using a query, which maybe textual, verbal, or visual, e.g., via a prototype image.
  • the system returns a set of results in response to the query.
  • the result set is selected to be diverse, that is, each of the results differs from the others along different axes relevant to the search query.
  • the results are presented in a 2 dimensional arrangement (such as a grid).
  • One result, the primary result may be displayed more prominently.
  • the other results are displayed in a layout relative to the primary result such that results that are most similar to each other are laid out near to each other and results that are most dissimilar from each other are laid out far from each other.
  • the user may select one or more results to indicate a request to refine the search to identify items more similar to those results.
  • the system then returns and displays a set of results more similar to the selected results and less similar to the non-selected results. In this way the user can interactively refine a search specification in order to more precisely encode intended search goals.
  • FIG. 1 illustrates an example environment in which aspects of the invention may be implemented.
  • FIG. 2 is a simplified block diagram of a computer system that can be used to implement aspects of the present invention.
  • FIGS. 3-7 and 10 A illustrate documents embedded in 2-dimensional embedding space.
  • FIG. 8 is a block diagram of various components of an embodiment of a system according to the invention.
  • FIG. 9 is a flow chart illustrating various logic phases through which a system according to the invention may proceed.
  • FIG. 10B illustrates certain documents from FIG. 10A in layout space.
  • a system can have several aspects, and different embodiments need not implement all of them: 1) a module for creating an initial query, 2) a module for obtaining a set of candidate results satisfying the initial query, 3) a module for determining the distance or similarity between candidate results or a module for embedding the candidate results in a vector space, 4) a module for sub-selecting a discriminating set of candidate results, 5) a module for arranging candidate results in 2 dimensions, 6) a module for obtaining user input with regard to the candidate results, 7) a module for refining the search query to incorporate information regarding the user input encoded as geometric or distance constraints with respect to the embedding or distance measures of 3, 8) a module for iteratively obtaining a set of candidate results satisfying the initial query and the geometric or distance constraints accumulated from user input.
  • FIG. 8 is a block diagram of various components of an embodiment of a system according to the invention. It includes an embedding module 820 which calculates an embedding of the source documents into an embedding space, and writes the embedding information, in association with identification of the documents, into a document collection database 816 .
  • a user interaction module 822 receives queries and query refinement input from a user, and provides them to a query processing module 824 .
  • the user interaction module 822 includes a computer terminal, whereas in another embodiment it includes only certain network connection components through which the system communicates with an external computer terminal.
  • the query processing module 824 interprets the queries as geometric constraints on the embedding space, and narrows the collection to develop a set of candidate documents which satisfy the geometric constraints.
  • Candidate spaces as used herein are also embedding spaces, and for example may constitute a portion of the embedding space of collection 816 .
  • query processing module may also perform a re-embedding of the candidate documents in embedding space.
  • a discriminative selection module 828 selects a discriminative set of the documents from the candidate space 826 and presents them to the user via user interaction module 822 .
  • User interaction module 822 may then receive further refinement queries from the user, which are handled as above, or it may receive a user commit indication, in which case the system takes some action 830 with respect to the user's selected document such as opening the document for the user or engaging in further search refinement.
  • the user refinement input may not require a further geometric constraint on the candidate space, but rather may involve only selection of a different discriminative set of documents from the existing candidate space 826 for presentation to the user.
  • the candidate space database may not be implemented as a separate database, but rather may be combined in various ways with the document collection embedding space 816 .
  • Candidate space may also be implied rather than physical in some embodiments.
  • FIG. 9 is a flow chart illustrating various logic phases through which a system according to the invention may proceed.
  • a collection of digital documents which, as used herein includes images, text, web-pages, catalog entries, and sections of documents
  • an initial query is optionally processed to yield an initial subset of digital documents satisfying the query results.
  • the term “subset” refers only to a “proper” subset.
  • This may be a conventional text query, for example.
  • the initial query is processed to yield a set of geometric constraints (possibly empty).
  • step 916 the geometric constraints are applied to the space of embedded digital documents or to the initial subset of digital documents to identify a set of candidate results.
  • step 918 a discriminative subset of documents is selected from the candidate results, and in step 920 the discriminative subset of documents is presented toward the user.
  • step 922 the user provides further input, for example by selecting or deselecting candidate results, by indicating a direction on the display with respect to one or more candidate results, or by providing a ranked order of the candidate results; or if satisfied with one of the candidate results, the user indicates to commit to that result. If the user input indicates further refinement, then the logic returns to step 914 to process the refinement query to yield further geometric constraints step 924 ). If not, then in step 926 the system takes action with respect to the user-selected document.
  • step 910 occurs continuously in the background, separately from the remainder of the steps, and updates the document collection in embedding space asynchronously with the remainder of the steps.
  • the logic of FIG. 9 can be implemented using processors programmed using computer programs stored in memory accessible to the computer systems and executable by the processors, by dedicated logic hardware, including field programmable integrated circuits, or by combinations of dedicated logic hardware and computer programs.
  • Each block in the flowchart or phase in a logic sequence describes logic that can be implemented in hardware or in software running on one or more computing processes executing on one or more computer systems.
  • each step of the flow chart or phase in a logic sequence illustrates or describes the function of a separate module of software.
  • the logic of the step is performed by software code routines which are distributed throughout more than one module.
  • the “embedding space”, into which digital documents are embedded by embedding module 820 and in step 910 , as used herein is a geometric space within which documents are represented.
  • the embedding space is a vector space, in which the features of a document define its “position” in the vector space relative to an origin. The position is typically represented as a vector from the origin to the document's position, and the space has a number of dimensions based on the number of coordinates in the vector. Vector spaces deal with vectors and the operations that may be performed on those vectors.
  • the embedding space is a metric space, which does not have a concept of position, dimensions or an origin. Distances among documents in a metric space are maintained relative to each other, rather than relative to any particular origin.
  • Metric spaces deal with objects combined with a distance between those objects and the operations that may be performed on those objects. For purposes of the current discussion these objects are significant in that there exist many efficient algorithms that operate on vector spaces and metric spaces. For example metric trees may be used to rapidly identify objects that are “close” to each other.
  • metric trees may be used to rapidly identify objects that are “close” to each other.
  • we embed objects into vector spaces and/or metric spaces In the context of a vector space this means that we define a function that maps objects to vectors in some vector space.
  • a metric space it means that we define a metric (or distance) between those objects that allows us to treat the set of all such objects as a metric space.
  • vector spaces allow the use of a variety of standard measures of distance (divergence) including the Euclidean distance. Other embodiments can use other types of embedding spaces.
  • the goal of embedding digital documents in a vector space is to place intuitively similar documents close to each other.
  • a common way of embedding text documents is to use a bag-of-words model.
  • the bag of words model maintains a dictionary.
  • Each word in the dictionary is given an integer index, for example, the word aardvark may be given the index 1, and the word zebra may be given the index 60,000.
  • Each document is processed by counting the number of occurrences of each dictionary word in that document.
  • a vector is created where the value at the i th index is the count for the i th dictionary word. Variants of this representation normalize the counts in various ways.
  • Such an embedding captures information about the content and therefor the meaning of the documents. Text documents with similar word distributions are close to each other in this embedded space.
  • documents may be embedded into a vector space.
  • images may be processed to identify commonly occurring features using, e.g., scale invariant feature transforms (SIFT), which are then binned and used in a representation similar to the bag-of-words embedding described above.
  • SIFT scale invariant feature transforms
  • embeddings created using deep neural networks, or other deep learning techniques.
  • a neural network can learn an appropriate embedding by performing gradient descent against a measure of dimensionality reduction on a large set of training data.
  • a kernel based on data and derive a distance based on that kernel.
  • These approaches generally use large neural networks to map documents, words, or images to high dimensional vectors.
  • an embedding into a vector space may also be defined implicitly via a kernel. In this case the explicit vectors may never be generated or used, rather the operations in the vectors space are carried out by performing kernel operations in the original space.
  • “Distance” between two documents in embedding space “corresponds to” a predetermined measurement of dissimilarity among documents. Preferably it is a monotonic function of the measurement of dissimilarity. Typically it equals the measurement of dissimilarity.
  • Example distances include the Manhattan distance, the Euclidean distance, and the Hamming distance.
  • Such distances may be defined in a variety of ways.
  • One typical way is via embeddings into a vector space.
  • Other ways include encoding the similarity via a Kernel.
  • Kernel By associating a set of documents with a distance we are effectively embedding those documents into a metric space. Documents that are intuitively similar will be close in this metric space while those that are intuitively dissimilar will be far apart.
  • kernels and distance functions may be learned. In fact, it may be useful to learn new distance functions on subsets of the documents at each iteration of the search procedure.
  • databases 816 and 826 may use commonly available means to store the data in, e.g., a relational database, a document store, a key value store, or other related technologies.
  • a relational database e.g., a relational database
  • a document store e.g., a document store
  • a key value store e.g., a document store
  • the original document contents e.g., pointers to them
  • indexing structures are critical.
  • documents are embedded in a vector space indexes may be built using, e.g., kd-trees.
  • documents are associated with a distance metric and hence embedded in a metric space metric trees may be used.
  • databases described herein are stored on one or more non-transitory computer readable media. As used herein, no distinction is intended between whether a database is disposed “on” or “in” a computer readable medium. Additionally, as used herein, the term “database” does not necessarily imply any unity of structure. For example, two or more separate databases, when considered together, still constitute a “database” as that term is used herein.
  • the initial query presented to the system in step 912 may be created and evaluated using a variety of standard techniques.
  • the query may be presented as a set of keywords entered via a keyboard or via speech; the query may be a natural language phrase, or sentence entered via a keyboard or via speech; or the query may be an audio signal, an image, a video, or a piece of text representing a prototype for which similar audio signals, images, videos, or text may be sought.
  • a variety of means are known by which such an initial query may be efficiently evaluated, e.g., searching a relational database, or using an inverted index.
  • the initial query may also be designed to simply return a random set of results.
  • Faceted search provides a means for users to constrain a search along a set of axes.
  • the faceted search might provide a slider that allows users to constrain the range of acceptable prices.
  • the search constraints created from an initial query and subsequent user input are used to identify a set of candidate results.
  • This may be achieved using a variety of means.
  • the initial query may be performed against a relational database whereby the results are then embedded in a vector or metric space. These results may then be indexed using, e.g., a kd-tree or a metric tree and searched to identify candidates that satisfy both the initial query and the constraints.
  • the initial query may also be converted to geometric constraints that are applied to the set of embedded documents. In this case the geometric representation of the constraints implied both by the initial query and the user input are combined and an appropriate index is used to identify embedded documents satisfying both sets of constraints.
  • a “geometric constraint” on an embedding space is a constraint that is described formulaically in the embedding space, rather than only by cataloguing individual documents or document features to include or exclude.
  • the constraint can be described in the form of a specified function which defines a hypersurface. Documents on one side of the hypersurface satisfy the constraint whereas documents on the other side do not.
  • a hyperplane may be defined in terms of dot products or kernels and requires that k(x,z)>0 for a fixed vector x and a candidate z. Likewise a conic constraint may require that k(x,z)>c for some constant c.
  • a geometric constraint In a metric embedding space, the constraint can be described in the form of a function of, for example, distances between documents.
  • a geometric constraint might take the form of ‘all documents within a specified distance from document X’, for example, or ‘all documents whose distance to document A is less than its distance to document B’.
  • Geometric constraints also may be combined using set operations, e.g., union, intersection to define more complex geometric constraints. They also may be created by taking transformations of any of the example constraints discussed.
  • constraints may be “hard” or “soft”. Hard constraints are those which must be satisfied in the sense that solutions must satisfy the conditions of all hard constraints. Soft constraints are those which need not be satisfied but candidate solutions may be penalized for each soft constraint that they don't satisfy. Solutions may be rejected in a particular embodiment if the accumulation of such penalties is too large. Constraints may be relaxed in some embodiments, for example hard constraints may be converted to soft constraints by associating them with a penalty, and soft constraints may have their penalties reduced.
  • Search queries may be ambiguous, or underspecified and so the documents satisfying a query may be quite diverse. For example, if the initial query is for a “red dress” the results may be quite varied in terms of their length, neckline, sleeves, etc.
  • This aspect of the module sub-selects a discriminating set of results. Intuitively the objective is to provide a set of results to the user such that selection or de-selection of those results provides the most informative feedback or constraints to the search algorithm.
  • One may think of this step as identifying an “informative” set of results, or a “diverse” set of results, or a “discriminating” set of results.
  • Discriminative selection module 828 performing step 918 , selects a discriminative subset of results in any of a variety of ways.
  • a subset of the results may be discriminative as it provides a diversity of different kinds of feedback that the user can select.
  • Diverse images may be selected as in, e.g., van Leuken, et. al., “Visual Diversification of Image Search Results”, in WWW '09 Proceedings of the 18th international conference on World wide web, pp. 341-350 (2009), incorporated by reference herein.
  • This diverse set is selected in order to provide the user with a variety of ways in which to refine the query at the next iteration. There are a variety of ways in which such a set may be identified. For example, farthest first traversal may be performed which incrementally identifies the “most” diverse set of results. Farthest first traversal requires only a distance measure and does not require an embedding. Farthest first traversal may also be initialized with a set of results; subsequent results are then the most different from that initial set.
  • discriminative subsets of candidate results include using an algorithm like PCA (principal component analysis) or kernel PCA to identify the key axes of variation in the complete set of results.
  • PCA principal component analysis
  • kernel PCA kernel PCA
  • Another means might consider the set of constraints that would results from the user selecting or deselecting a given document. This set of constraints may be considered in terms of the candidate results it would yield.
  • a discriminative subset may be selected so that the sets of candidate results produced by selecting any of the documents in that discriminative subset are as different as possible.
  • discriminativeness of a particular set of documents in a collection of documents is the least number of documents in the collection that are excluded as a result of user selection of any document in the set. That is, if user selection of different documents in the particular set results in excluding different numbers of documents in the collection, then the set's “discriminativeness” is considered herein to be the least of those numbers. Note that either the discriminative set of documents, or the formula by which user selection of a document determines which documents are to be excluded, or both, should be chosen such that the union of the set of documents excluded by selecting any of the documents in a discriminative set equals the entire collection of documents.
  • the “average discriminativeness” of a set of size n documents in a collection of documents is the average, over all sets of size n documents in the collection of documents, of the discriminativeness of that set. Also as used herein, one particular set of documents can be “more discriminative” than another set of documents if the discriminativeness of the first set is greater than the discriminativeness of the second set.
  • the selection module 828 performing step 918 , selects a set of N1>1 documents from the current candidate space 826 , which is more discriminative than the average discriminativeness of sets of size N1 documents in the candidate space. Even more preferably, selection module 828 performing step 918 selects a set which is at least as discriminative as all other sets of size N1 documents in the current candidate space.
  • the aim of the discriminative results presentation to the user in step 920 by user interaction module 822 is to provide the user with a framework in which to refine the query constraints.
  • results may be presented as a two dimensional grid.
  • Results should be placed on that grid in a way that allows the user to appreciate the underlying distances between those results (as defined using a distance measure or embedding).
  • One way to do this would be to ensure that results that are far from each other with respect to the distance measure are also displayed far from each other on the grid.
  • Another way would be to project the embedding space onto two dimensions for example using multidimensional scaling (MDS) (for example see: Jing Yang, et. al., Semantic image browser: Bridging information visualization with automated intelligent image analysis, Proc. IEEE Symposium on Visual Analytics Science and Technology (2006), incorporated herein by reference).
  • MDS multidimensional scaling
  • Yet another way would be to sub-select axes in the embedding space and position results along those axes.
  • layouts contemplated include 2 dimensional organizations not on a grid (possibly including overlapping results), 3 dimensional organizations analogous to the 2 dimensional organizations. Multi-dimensional organizations analogous to the 2 and 3 dimensional organizations with the ability to rotate around one or more axes.
  • M-dimensional layout can be used, where M>1.
  • the number of dimensions in the presentation layout need not be the same as the number of dimensions in the embedding space.
  • layouts include hierarchical organizations or graph based layouts.
  • the document placement in the layout space should be indicative of the relationship among the documents in embedding space.
  • the distance between documents in layout space should correspond (monotonically, if not linearly) with the distance between the same documents in embedding space.
  • three documents are collinear in embedding space, advantageously they are placed collinearly in layout space as well.
  • collinearity in layout space with a candidate document which the system identifies as the most likely target of the user's query (referred to herein as the primary candidate document) indicates collinearity in the embedding space with the primary candidate document.
  • embedding space typically has a very large number of dimensions, and in high dimensional spaces very few points are actually collinear.
  • documents presented collinearly in layout space indicate only “substantial” collinearity in embedding space.
  • the embedding space is such that each document has a position in the space (as for a vector space)
  • three documents are considered “substantially collinear” in embedding space if the largest angle of the triangle formed by the three documents in embedding space is greater than 160 degrees.
  • a group of three documents are considered collinear if the sum of the two smallest distances between pairs of the documents in the group in embedding space equals the largest distance between pairs of the documents in the group in embedding space.
  • the three documents are considered “substantially collinear” if the sum of the two smallest distances exceeds the largest distance by no more than 10%.
  • colllinearity and substantially collinearity do not include the trivial cases of coincidence or substantial coincidence.
  • User interaction module 822 provides the user with a user interface (UI) which allows the user to provide input in a variety of ways. For example, the user may click on a single result to select it, or may swipe in the direction of a single result to de-select it. Similarly, the user may select or deselect multiple results at a time. For example, this may be done using a toggle selector on each result. The user might also implicitly select a set of results by swiping in the direction of a result indicating a desire for results that are more like that result “in that direction”. In this case “in that direction” means that the differences between the primary result and the result being swiped should be magnified.
  • UI user interface
  • next set of results should be more like the result being swiped and less like the “primary result”.
  • This concept may be generalized by allowing the user to swipe “from” one result “to” another result. In this case new results should be more like the “to” result and less like the “from” result.
  • the UI can provide the user with the ability (e.g., via a double-click, or a pinch) to specify that the next set of results should be more like a specific result than any of the other results displayed. This is analogous to zooming on a map. Conversely, the UI can provide the ability to specify that the next set of results should be like a particular selection but more diverse than the currently selected set of results.
  • the UI may also provide the ability for the user to specify that a different set of similarly diverse images be provided.
  • the system then incorporates the user's input to create a refined query.
  • the refined query includes information regarding the initial query and information derived from the iterative sequence of refinements made by the user so far.
  • This refined query may be represented as a set of geometric constraints that focus subsequent results within a region of the embedding space. Likewise, it may be represented as a set of distance constraints whose intersection defines the refined candidate set of results. It may also be represented as a path through the set of all possible results.
  • the refined query may include constraints that require subsequent results to be within a specified distance of one of the selected candidate results.
  • the refined query may include constraints that require subsequent results to be closer (with respect to the distance measure) to one candidate result than to another.
  • a system according to the invention may have the ability to relax, tighten, remove, or modify constraints that it determines are inappropriate.
  • Such data structures include metric trees, kd-trees, R-trees, universal B-trees, X-trees, ball trees, locality sensitive hashes, and inverted indexes.
  • the system uses a combination of such data structures to identify the next set of candidate results based on the refined query.
  • User behavior data may be collected by a system according to the invention and used to improve or to specialize the search experience.
  • many ways of expressing distances or similarities may be parameterized and those parameters may be fit.
  • a similarity defined using a linear combination of kernels may have the coefficients of that linear combination tuned based on user behavior data. In this way the system may adapt to individual (or community, or contextual) notions of similarity.
  • FIG. 3 illustrates a set of documents embedded in 2-dimensional space. Aspects of the invention envision embedding documents in spaces of large dimensionality, hence two dimensions is for illustration purposes only.
  • the space 310 contains documents, e.g., 321 , 322 . Each pair of documents has a distance 330 between them.
  • FIG. 4 illustrates the set of documents from FIG. 3 in addition to a circular geometric constraint 410 .
  • Those documents inside the circle, e.g., 421 , 422 are said to satisfy the constraint.
  • Aspects of the invention express queries and user input in the form of such geometric constraints.
  • the documents that satisfy the constraints are the current results of the query. As the user provides further input additional constraints may be added, or existing constraints may be added or removed.
  • FIG. 5 illustrates the set of documents from FIG. 3 in addition to a non-circular geometric constraint 510 .
  • Aspects of the invention envision geometric constraints of arbitrary shape, and unions, intersections and differences of such constraints.
  • FIG. 6 illustrates a means by which the circular constraint of FIG. 4 may be updated in response to user input.
  • the original circular constraint 610 may be modified by increasing its radius to produce circular constraint 620 , or by decreasing its radius to produce constraint 630 . These modifications are done in response to user input.
  • the set of documents satisfying these constraints will change as the constraints are modified thus reducing or expanding the set of images considered for display to the user.
  • FIG. 7 illustrates a means by which a discriminative subset of documents may be selected for presentation to the user.
  • the documents highlighted, e.g., 711 , 712 are distinct from each other and from the others contained in the circular constraint region.
  • FIG. 10A illustrates a set of documents in embedding space, in which query processing module 824 has narrowed the collection to those documents within the circle 1020 , and has identified primary result document 1018 .
  • discriminative selection module 828 has selected documents 1010 , 1012 , 1014 and 1016 as the discriminative set to present to the user. It can be seen that in embedding space, documents 1012 , 1018 and 1016 are substantially collinear, and that documents 1010 , 1018 and 1014 are substantially collinear.
  • FIG. 10B illustrates how the system may present the set of documents in layout space.
  • the broken lines are implied, rather than visible.
  • the specific positions of the documents do not necessarily match those in embedding space, in part because dimensionality of the space has been reduced.
  • documents which were substantially collinear in embedding space are collinear in layout space.
  • the broken lines in FIG. 10A represent dimensions in embedding space along which the candidate documents differ
  • the placement of the documents in layout space in FIG. 10B are indicative of those same dimensions.
  • the relative distances among the documents along each of the lines of collinearity in layout space also are indicative of the relative distances in embedding space.
  • One implementation allows users to search a personal photograph collection.
  • Users are initially shown an arbitrary photograph (the primary result), e.g., the most recent photograph taken or viewed. This is displayed in the center of a 3 ⁇ 3 grid of photographs from the collection.
  • Each of the photographs is selected to be close (defined below) to the primary result but different from each other along different axes relative to the primary result.
  • the primary result is a photograph taken with family last week at home
  • other photographs may be a) with the family last year at home, b) with the family last week outdoors, c) without the family last week at home, etc.
  • the system may place two photographs on opposite sides of the primary result which are along the same axis but differ from each other in their positions along that axis. For example, the photo placed on the left side may show family member A more prominently than in the primary result, while the photo placed on the right side may show family member A less prominently than in the primary result.
  • the user selects one of the 9 photographs which then becomes the primary result. This is then laid out in an updated 3 ⁇ 3 grid of photographs again “close” to it but different from each other.
  • photographs may be considered similar with respect to a number of criteria, including:
  • a normalization function can be applied in order that distances along different axes are comparable to each other.
  • the “scale” at which the user is searching changes.
  • This scale specifies how “close” the photos in the result set are to the primary result. More precisely all photos in the result set must have a “distance” less than some threshold. As the scale increases or decreases this threshold increases or decreases.
  • Another implementation looks at searching for accessories (apparel, furniture, apartments, jewelry, etc).
  • the user searches using text, speech, or with a prototype image as an initial query.
  • a user searches for “brown purse” using text entry.
  • the search engine responds by identifying a diverse set of possible results, e.g., purses of various kinds and various shades of brown. These results are laid out in a 2 dimensional arrangement (for example a grid), whereby more similar results are positioned closer to each other and more different results are positioned relatively far from each other.
  • the user selects one or more images, for example using radio buttons.
  • the image selections are then used by the search engine to define a “search direction” or a vector in the embedding space along which further results may be obtained.
  • Documents are encoded in an embedding space such as a vector space or metric space (via a distance). Searches proceed as a sequence of query refinements. Query refinements are encoded as geometric constraints over the vector space or metric space. Discriminative candidate results are displayed to provide the user with the ability to add discriminative constraints. User inputs, e.g., selecting or deselecting results, are encoded as geometric constraints.
  • the documents may embedded after the initial query is process and only those documents satisfying the query may be embedded.
  • the documents may be re-embedded using a different embedding at any point in the process. In this case, the geometric constraints would be re-interpreted in the new embedding.
  • the geometric constraints may be augmented at any point with non-geometric constraints.
  • the candidate results can be filtered in a straightforward way to select only those satisfying the non-geometric constraints.
  • the interaction can be augmented with faceted search, text, or speech inputs.
  • the geometric constraints can be managed together with a set of non-geometric constraints.
  • An example implementation may proceed through these steps:
  • the above method may be viewed either from the viewpoint of the user interacting with a computer system, or the viewpoint of a computer system interacting with a user, or both.
  • the concept may be generalized to refer to “digital documents” rather than images, where digital documents include audio, video, text, html, multimedia documents and product listings in a digital catalog, in addition to images.
  • the concept may also be generalized so that the initial collection obtained at step 1 is obtained as the result of the user performing a search (query) within another information retrieval system or search engine.
  • the concept may also be generalized so that rather than reducing the threshold at step 8, the user interface provides for the ability to decrease or increase the threshold or leave it unchanged.
  • the concept may also be generalized so that at steps 1, and 6 there are two collections of prototype images and at step 2 the system identifies images with distance less than threshold T 1 of the first set, and greater than T 2 of the second set.
  • the concept may also be generalized so that at one iteration of step 6 the user selects image(s) along a first subset of at least one axis, and at another iteration of step 6 the user selects image(s) along a second subset of at least one axis, where the second subset of axes contains at least one axis not included in the first subset of axes.
  • FIG. 1 illustrates an example environment in which aspects of the invention may be implemented.
  • the system includes a user computer 110 and a server computer 112 , connected to each other via a network 114 such as the Internet.
  • the server computer 112 has accessibly thereto the database 816 identifying documents in association with embedding information, such as relative distances and/or their positions in a vector space.
  • the user computer 110 also in various embodiments may or may not have accessibly thereto a database 118 identifying the same information.
  • embedding module 820 (which may for example be the server computer 112 or a separate computer system or a process running on such a computer) analyzes a collection of documents to extract embedding information about the documents. For example, if the documents are photographs, the embedding module 820 may include a neural network and may use deep learning to derive embedding image information from the photographs.
  • embedding module 820 may derive a library of image classifications (axes on which a given photograph may be placed), each in association with an algorithm for recognizing in a given photograph whether (or with what probability) the given photograph satisfies that classification. Then the embedding module 820 may apply its pre-developed library to a smaller set of newly provided photographs, such as the photos currently on the user computer 110 , in order to determine embedding information applicable to each photograph. Either way, the embedding module 820 writes into the database 816 the identifications of the collection of documents that the user may search, each in association with its embedding information.
  • the embedding information that embedding module 820 writes into database 816 may be provided from an external source, or entered manually.
  • the iterative identification steps described above can be implemented in a number of different ways.
  • all computation takes place on the server computer 112 , as the user iteratively searches for a desired document.
  • the user operating the user computer 110 , sees all results only by way of a browser.
  • the server computer 112 transmits its entire database 118 of documents in embedding space (or a subset of that database) to the user computer 110 , which writes it into its own database 118 . All computation takes place on the user computer 110 in such an embodiment, as the user iteratively searches for a desired document. Many other arrangements are possible as well.
  • FIG. 2 is a simplified block diagram of a computer system 210 that can be used to implement software incorporating aspects of the present invention.
  • the drawing represents both an embodiment of user computer 110 and server computer 112 . While the above-described methods indicate individual logic steps or modules for carrying out specified operations, it will be appreciated that each step or module actually causes the computer system 210 to operate in the specified manner.
  • Computer system 210 typically includes a processor subsystem 214 which communicates with a number of peripheral devices via bus subsystem 212 .
  • peripheral devices may include a storage subsystem 224 , comprising a memory subsystem 226 and a file storage subsystem 228 , user interface input devices 222 , user interface output devices 220 , and a network interface subsystem 216 .
  • the input and output devices allow user interaction with computer system 210 .
  • Network interface subsystem 216 provides an interface to outside networks, including an interface to communication network 218 , and is coupled via communication network 218 to corresponding interface devices in other computer systems.
  • Communication network 218 may comprise many interconnected computer systems and communication links.
  • communication links may be wireline links, optical links, wireless links, or any other mechanisms for communication of information, but typically it is an IP-based communication network. While in one embodiment, communication network 218 is the Internet, in other embodiments, communication network 218 may be any suitable computer network.
  • NICs network interface cards
  • ICs integrated circuits
  • ICs integrated circuits
  • macrocells fabricated on a single integrated circuit chip with other components of the computer system.
  • User interface input devices 222 may include a keyboard, pointing devices such as a mouse, trackball, touchpad, or graphics tablet, a scanner, a touch screen incorporated into the display, audio input devices such as voice recognition systems, microphones, and other types of input devices.
  • pointing devices such as a mouse, trackball, touchpad, or graphics tablet
  • audio input devices such as voice recognition systems, microphones, and other types of input devices.
  • use of the term “input device” is intended to include all possible types of devices and ways to input information into computer system 210 or onto computer network 218 . It is by way of input devices 222 that the user provides queries and query refinements to the system.
  • User interface output devices 220 may include a display subsystem, a printer, a fax machine, or non-visual displays such as audio output devices.
  • the display subsystem may include a cathode ray tube (CRT), a flat-panel device such as a liquid crystal display (LCD), a projection device, or some other mechanism for creating a visible image.
  • the display subsystem may also provide non-visual display such as via audio output devices.
  • output device is intended to include all possible types of devices and ways to output information from computer system 210 to the user or to another machine or computer system. It is by way of output devices 220 that the system presents query result layouts toward the user.
  • Storage subsystem 224 stores the basic programming and data constructs that provide the functionality of certain embodiments of the present invention.
  • the various modules implementing the functionality of certain embodiments of the invention may be stored in storage subsystem 224 .
  • These software modules are generally executed by processor subsystem 214 .
  • Memory subsystem 226 typically includes a number of memories including a main random access memory (RAM) 230 for storage of instructions and data during program execution and a read only memory (ROM) 232 in which fixed instructions are stored.
  • File storage subsystem 228 provides persistent storage for program and data files, and may include a hard disk drive, a floppy disk drive along with associated removable media, a CD ROM drive, an optical drive, or removable media cartridges.
  • the databases and modules implementing the functionality of certain embodiments of the invention may have been provided on a computer readable medium such as one or more CD-ROMs, and may be stored by file storage subsystem 228 .
  • the host memory 226 contains, among other things, computer instructions which, when executed by the processor subsystem 214 , cause the computer system to operate or perform functions as described herein. As used herein, processes and software that are said to run in or on “the host” or “the computer”, execute on the processor subsystem 214 in response to computer instructions and data in the host memory subsystem 226 including any other local or remote storage for such instructions and data.
  • Bus subsystem 212 provides a mechanism by which the various components and subsystems of computer system 210 communicate with each other as intended. Although bus subsystem 212 is shown schematically as a single bus, alternative embodiments of the bus subsystem may use multiple busses.
  • Computer system 210 itself can be of varying types including a personal computer, a portable computer, a workstation, a computer terminal, a network computer, a television, a mainframe, a server farm, or any other data processing system or user device.
  • user computer 110 may be a hand-held device such as a tablet computer or a smart-phone. Due to the ever-changing nature of computers and networks, the description of computer system 210 depicted in FIG. 2 is intended only as a specific example for purposes of illustrating the preferred embodiments of the present invention. Many other configurations of computer system 210 are possible having more or less components than the computer system depicted in FIG. 2 .
  • a computer readable medium is one on which information can be stored and read by a computer system. Examples include a floppy disk, a hard disk drive, a RAM, a CD, a DVD, flash memory, a USB drive, and so on.
  • the computer readable medium may store information in coded formats that are decoded for actual use in a particular data processing system.
  • a single computer readable medium may also include more than one physical item, such as a plurality of CD-ROMs or a plurality of segments of RAM, or a combination of several different kinds of media.
  • the term does not include mere time varying signals in which the information is encoded in the way the signal varies over time.
  • a given event or value is “responsive” to a predecessor event or value if the predecessor event or value influenced the given event or value. If there is an intervening processing element, step or time period, the given event or value can still be “responsive” to the predecessor event or value. If the intervening processing element or step combines more than one event or value, the signal output of the processing element or step is considered “responsive” to each of the event or value inputs. If the given event or value is the same as the predecessor event or value, this is merely a degenerate case in which the given event or value is still considered to be “responsive” to the predecessor event or value. “Dependency” of a given event or value upon another event or value is defined similarly.
  • the “identification” of an item of information does not necessarily require the direct specification of that item of information.
  • Information can be “identified” in a field by simply referring to the actual information through one or more layers of indirection, or by identifying one or more items of different information which are together sufficient to determine the actual item of information.
  • the term “indicate” is used herein to mean the same as “identify”.

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US14/494,364 US20150331908A1 (en) 2014-05-15 2014-09-23 Visual interactive search
CN201580038513.4A CN107209762B (zh) 2014-05-15 2015-05-04 视觉交互式搜索
EP15760512.2A EP3143523B1 (fr) 2014-05-15 2015-05-04 Recherche interactive visuelle
US15/311,163 US10503765B2 (en) 2014-05-15 2015-05-04 Visual interactive search
DE112015002286.4T DE112015002286T9 (de) 2014-05-15 2015-05-04 Visuelle interaktive suche
GB1621341.5A GB2544660A (en) 2014-05-15 2015-05-04 Visual interactive search
JP2016567798A JP2017518570A (ja) 2014-05-15 2015-05-04 視覚的対話型検索
PCT/IB2015/001267 WO2015173647A1 (fr) 2014-05-15 2015-05-04 Recherche interactive visuelle
TW104114144A TW201606537A (zh) 2014-05-15 2015-05-04 視覺交互搜尋
US15/295,930 US10606883B2 (en) 2014-05-15 2016-10-17 Selection of initial document collection for visual interactive search
US15/295,926 US20170039198A1 (en) 2014-05-15 2016-10-17 Visual interactive search, scalable bandit-based visual interactive search and ranking for visual interactive search
US15/373,897 US10102277B2 (en) 2014-05-15 2016-12-09 Bayesian visual interactive search
US16/681,514 US11216496B2 (en) 2014-05-15 2019-11-12 Visual interactive search

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JP2017518570A (ja) 2017-07-06
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US10503765B2 (en) 2019-12-10
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US20170075958A1 (en) 2017-03-16

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