WO2015032670A1 - Procédé de classification d'images et dispositif correspondant - Google Patents
Procédé de classification d'images et dispositif correspondant Download PDFInfo
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- WO2015032670A1 WO2015032670A1 PCT/EP2014/068166 EP2014068166W WO2015032670A1 WO 2015032670 A1 WO2015032670 A1 WO 2015032670A1 EP 2014068166 W EP2014068166 W EP 2014068166W WO 2015032670 A1 WO2015032670 A1 WO 2015032670A1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
- G06F16/2457—Query processing with adaptation to user needs
- G06F16/24578—Query processing with adaptation to user needs using ranking
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
- G06F16/951—Indexing; Web crawling techniques
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
Definitions
- the present disclosure relates to the field of classification of images.
- Image classification aims to determine whether a given image contains a specific visual concept (e.g. car, cow, sea, forest).
- images are represented by extracting from them a feature vector.
- the feature vector is a high-dimensional vector of numerical features that represent an image.
- a widely used feature vector is the bag-of- words vector consisting of a histogram of quantized image patches centered at corners detected in the image. Throughout this document, if not explicitly mentioned, it is implicitly assumed that computations that use images in fact use the corresponding feature vectors.
- image classification a training set is obtained by manual annotation of a set of images.
- annotated training data set For example, for the concept "cat", a person goes through, e.g., 1000 images of a set of images, and assigns to each image a label of "1 " if a cat is present in the image and a "0" otherwise.
- the resulting set of images and labels are referred to as an "annotated training data set".
- This annotated training data set is used to learn a classifier, and this classifier can be used to assign a label to an un- annotated set of images without manual intervention.
- the classifier can be thought of as an algorithm which classifies the given image and assigns it to a corresponding visual concept. The assignment is based on the visual content present in an image.
- SVM support vector machines
- K-nearest neighbors K-nearest neighbors
- the annotation of the training set is a manual task, and is laborious and expensive.
- the resulting classifier vectors are fixed and thus inflexible for learning new visual concepts. Yet the un-annotated dataset will likely vary, e.g. when the un-annotated data set is a user collection of images, images are deleted, replaced, and added to the user collection.
- a classifier for the visual concept specified by the text query can then be learned (corresponding to previous mentioned a step (ii)) and the resulting classifier vector is used as in step (iii) for the classification of an un-annotated data set.
- two problems can be observed with this prior art on-the-fly image classification:
- the disclosure comprises a method of classification of images, the method comprising: receiving a query for a visual concept; executing a first image search for the query visual concept in a first set of images and extracting first image feature descriptors from images returned by the first image search that correspond to said queried visual concept, forming a positive set of positive image feature descriptors of images; determining a set of attribute visual concepts that are related to the queried visual concept; executing a second image search in the first set of images for the set of attribute visual concepts and extracting second image feature descriptors from images returned by the second image search, forming an attribute set of attribute image feature descriptors of images; computing a first score for each of the positive image feature descriptors in the positive set by calculating inner products between each of the positive image feature descriptors in the positive set and the attribute image feature descriptors in the attribute set, and removing positive image feature descriptors from the positive set having a first score that is under a determined threshold, forming a pruned positive
- the method further comprises a step of computing a third score for each of the images in the un-annotated data set by calculating inner products between image feature descriptors of each of the images in the un-annotated set and the image feature descriptors in the attribute set, and combining the third score with the second score obtained in the classifying images step to obtain a fourth score for each of the images in the un-annotated set, and classifying of the images in the un- annotated set according to the fourth score.
- the determining of said set of attribute visual concepts that are related to said queried visual concept comprises a step of retrieving a list of attributes that are related to the queried visual concept from a relational image set.
- the determining of said set of attribute visual concepts that are related to said queried visual concept comprises a step of retrieving a list of attributes that are related to the queried visual concept from information comprised in the query.
- the determining of said set of attribute visual concepts that are related to said queried visual concept comprises a step of retrieving a list of attributes that are related to the queried visual concept through language processing of the query.
- the determining of said set of attribute visual concepts that are related to said queried visual concept comprises a step of retrieving a list of attributes that are related to the queried visual concept through a word search in textual data, whereby words that have a high frequency of occurrence relative to other words are retained for being comprised in the list of attributes.
- the present disclosure also comprises a device for classification of images, the device comprising: an interface for receiving a query for a visual concept; a processing module for executing a first image search for the query visual concept in a first set of images and extracting first image feature descriptors from images returned by the first image search that correspond to said queried visual concept, forming a positive set of positive image feature descriptors of images; a processing module for determining a set of attribute visual concepts that are related to the queried visual concept; a processing module for executing a second image search in the first set of images for the set of attribute visual concepts and for extracting second image feature descriptors from images returned by the second image search, forming an attribute set of attribute image feature descriptors of images; a processing module for computing a first score for each of the positive image feature descriptors in the positive set by calculating inner products between each of the positive image feature descriptors in the positive set and the attribute image feature descriptors in the attribute set, and for removing positive image feature descriptors
- Figure 1 is a traditional prior-art classification system.
- Figure 2 is an on-the-fly prior-art classification system that addresses some of the problems of the traditional prior-art classification system of figure 1 .
- Figure 3 is an attribute-based on-the-fly classification system according a particular example embodiment.
- Figure 4 is a variant embodiment of the on-the-fly classification system of figure 3.
- Figure 5 is a flow chart of a particular embodiment of the disclosed on-the-fly classification system.
- Figure 6 is a device implementing a particular embodiment of the disclosed on-the-fly classification system.
- Figure 1 is a traditional prior-art classification system which has briefly been discussed in the first part of the background section.
- Element 10 represents a positive data set X+ that is collected in a first step (i) of collecting an annotated training set consisting of positive images representing the visual concept.
- Element 1 1 is a negative dataset X- of negative images representing the universe of opposite non matching concepts.
- the annotated data is then used to train a visual classifier for the specified visual concept, using an SVM training algorithm.
- This produces a vector, or classifier ⁇ W in the image feature space.
- the resulting classifier ⁇ W is then applied to all un-annotated images of an un-annotated data set (element 14) (e.g. a 'raw' user collection of images that has not been classified), which amounts to computing the inner product between the classifier vector and the image feature vector of the un-annotated image.
- Figure 2 is a prior-art on-the-fly classification system (Chatfield) that has briefly been discussed in the second part of the background section and that addresses some problems of the traditional prior art of figure 1 .
- the classification system of figure 2 is referred to as an "on-the-fly" classification system and is entirely machine processed to the contrary of the traditional classification system that comprises a manual annotation step of the training set. This machine processing makes it possible to classify with a delay that is much shorter than is possible with the traditional classification system. Processing is as follows.
- a visual concept, specified as a text query (element 17) is to be searched for in an un-annotated data set (element 14), e.g. a user collection of unclassified images.
- the textual query is fed to an image search engine (element 18).
- the images returned by the image search engine form a positive data set (element 20).
- Image features are extracted by an image feature vector extractor (element 19) from the images returned by the image search engine and are converted in vector form.
- the obtained vectors are associated with the images of the positive data set.
- a fixed pool of very diverse images is used as a negative data set (21 ), With this positive and negative data sets, a classifier for the visual concept specified by the text query is learned using the process that has been described for figure 1 . Disadvantages of this prior-art on-the-fly classification system have already briefly been discussed: (i) among the images retrieved from the image search engine, are many incorrect representatives of the queried visual concept.
- Figure 3 is an attribute-based on-the-fly classification system according an example embodiment. Attributes are high level visual characteristics that the human brain uses when classifying images in categories. For example a class of animals can also be represented by attributes four-legged, furry, and striped. Attribute identifiers are built based on the same system setup as shown in figure 2, with positive images being representative of the attribute, and negative images that are not representative of the attribute. For example, to train an image classifier for the attribute "furry”, positive images containing furry animals (e.g., cats, bears, ...) and negative images containing animals without fur (e.g., lizards, birds) are used.
- furry animals e.g., cats, bears, .
- negative images containing animals without fur e.g., lizards, birds
- a k is used to denote the attribute classifier thus learned for attribute number k.
- a database of attribute classifiers is used. This attribute database can be built offline and/or built/extended on-the-fly.
- the classification system of figure 3 shows the following elements that are additional to the prior-art on- the-fly image classification system of figure 2: attribute engine 34, attribute database 35, positive dataset pruning function 36, and optionally a hybrid attribute/SVM ranking function 33. Elements in figure 3 that have been discussed previously in the context of figure 2 have the same numbers and are not discussed here further.
- SVM classification other classification methods can be used, such as the K-nearest neighbor classifier.
- the latter does not produce a vector W but a partition of feature space, akin to Voronoi cells.
- the positive set with feature vectors of images corresponding to the query visual concept, and the negative with feature vectors of images not corresponding to the query are fed to a vector classifier. This defines a partition of the image feature space that consists of positive and negative regions of the feature space.
- Attribute engine maps a query visual concept (e.g., "sheep") into a list of related attributes (e.g. "sheep" related attributes are ⁇ furry,hooves ⁇ ). This function can be implemented according to several variant embodiments:
- relational image data sets like ImageNet. Such relational image data sets are organized into a network of semantic/conceptual relationships, with a representative image set per concept. Given a query visual concept, neighboring concepts serve as attributes, and the corresponding representative images of the neighboring concepts is used to extend the attribute database block as described hereafter.
- Annotating a dataset like ImageNet has been a crowd sourced task that has begun in the 1980s.
- the user provides query-related attributes as part of the query specification. This is done for example using a pre-defined syntax to enter the concept and related concept(s): e.g., ⁇ visual concept>: ⁇ attribute 1 , attribute 2 ⁇ like "sheep: ⁇ furry, hooves ⁇ ”) is used or different text boxes are used to enter the query, thereby clarifying which elements of the query correspond to the visual concept and which elements of the query correspond to the query related attributes.
- natural language processing techniques are used to extract visual concepts and related attributes from the user query text (e.g., nouns represent the visual concept, adjectives in the query represent the query attributes),
- the text corresponding to the user query is fed to a textual search engine to retrieve attributes that are related to the query. Words that have a relative high frequency of appearance in the retrieved documents are used as attributes.
- Attribute Database (element 35): The list of attributes (e.g., ⁇ furry, hooves ⁇ ) at the output of the attribute engine 34 is fed to this function.
- the attribute database 35 contains a large storage of such attribute classifiers. Since attributes can be shared by a wide range of visual concepts, it is conceivable to build a useful, generic set of attributes. For example, these are color attributes ("red”, “blue”, “green”), shape attributes
- a variant embodiment for creating the attribute database is to extend it while using the on-the-fly system: each attribute in the input list of attributes is fed independently to the on-the-fly system in Figure 3. The resulting attribute classifier is stored in the attribute database.
- the matrix A T X + is normalized along rows to make different attribute scores comparable across images by removing the mean and dividing by the standard deviation in a row-wise manner, thereby obtaining a normalized matrix A n0 rm:
- the resulting attribute scores s are sorted in descending order and the last "n" images are discarded.
- the remaining images form the positive data set 30.
- the pruned positive data set 30 of image feature vectors is used as an input in the classification process of the un-annotated data set 14, as for example described for figure 2, using a SVM classifier 12, that also receives as an input the image feature vectors of a set of negative images representing visual concepts that do not correspond to the query visual concept, the SVM classifier produces a vector W.
- This vector is used in a SVM ranker 33 to rank the images in the un-annotated data set 14 according to their relevance to the query visual concept.
- Figure 4 is a variant embodiment of the on-the-fly classification system of figure 3.
- the classification system described above comprises previous discussed functions (1 ) to (3), and further comprises an optional additional function :
- Hybrid attribute / SVM ranking function (element 33) :
- the matrix A which is the output of the attribute database block, is additionally used to rank the un-annotated dataset X T (element 14).
- a hybrid approach is thus used that mixes both the SVM classifier score and the attribute classifier score: a: similar to steps (a) to (c) of above step (3) (positive data set pruning), in order to get an attribute score for the un-annotated data Xj. classification of elements in matrix Xj using the attributes in matrix A, giving a matrix ⁇ ⁇ ⁇ ; normalization of this matrix, giving a matrix T n0 rm:
- Figure 5 is a flow chart of a particular embodiment of the method for classification of images.
- a query for a visual concept is received.
- a first image search for the query visual concept in a first set of images is executed, and image feature descriptors are extracted from images returned by the first image search, thereby forming a positive set of positive image feature descriptors of images corresponding to the query visual concept.
- a set of attribute visual concepts is determined that are related to the query visual concept.
- a second image search is executed in the first set of images for the attribute visual concepts and image feature descriptors are extracted from images returned by the second image search, thereby forming an attribute set of attribute image feature descriptors of images corresponding to the attribute visual concepts.
- a first score is computed for each of the image feature descriptors in the positive set by calculating inner products between each of the positive image feature descriptors and the attribute image feature descriptors, and positive image feature descriptors are removed from the positive set having a first score that is under a determined threshold, teherby forming a pruned positive set.
- a step 55 the pruned positive set and a negative set with feature vectors of images not corresponding to the query visual concept are fed to a vector classifier, thereby partitioning the image feature space in a positive feature space region and in a negative feature space region .
- images in an un-annotated set of images are classified by computing an inner product between vector W and each of the image feature descriptors of the un- annotated set thereby obtaining a second score for each image in the un- annotated set, and classifying of the images in the un-annotated set according to the second score.
- Figure 6 is a device 600 implementing a particular embodiment of the described method.
- the device comprises: an interface (60) for receiving a query for a visual concept; a processing module (62) for executing a first image search for the query visual concept in a first set of images and extracting first image feature descriptors from images returned by the first image search that correspond to the queried visual concept, forming a positive set of positive image feature descriptors of images; a processing module (62) for determining a set of attribute visual concepts that are related to the query visual concept; a processing module (62) for executing a second image search in the first set of images for the set of attribute visual concepts and for extracting second image feature descriptors from images returned by the second image search, forming an attribute set of attribute image feature descriptors of images; a processing module (62) for computing a first score for each of the positive image feature descriptors in the positive set by calculating inner products between each of the positive image feature descriptors in the positive set and the attribute image feature descriptors
- aspects of the present principles can be embodied as a system, method or computer readable medium. Accordingly, aspects of the present principles can take the form of an entirely hardware embodiment, en entirely software embodiment (including firmware, resident software, micro-code and so forth), or an embodiment combining hardware and software aspects that can all generally be defined to herein as a "circuit", "module” or “system”. Furthermore, aspects of the present principles can take the form of a computer readable storage medium. Any combination of one or more computer readable storage medium(s) can be utilized.
- a computer readable storage medium can take the form of a computer readable program product embodied in one or more computer readable medium(s) and having computer readable program code embodied thereon that is executable by a computer.
- a computer readable storage medium as used herein is considered a non-transitory storage medium given the inherent capability to store the information therein as well as the inherent capability to provide retrieval of the information there from.
- a computer readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing.
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Abstract
Selon l'invention, une interrogation textuelle spécifiée par l'utilisateur est envoyée à un moteur de recherche d'images. Le images retournées sont utilisées comme données d'apprentissage pour apprendre un classificateur pour la catégorie de classe spécifiée par l'interrogation textuelle. Le procédé utilise des classificateurs d'attributs pour améliorer le système de classification d'images à la volée.
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US11222064B2 (en) * | 2015-12-31 | 2022-01-11 | Ebay Inc. | Generating structured queries from images |
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Cited By (6)
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US10922327B2 (en) | 2013-09-20 | 2021-02-16 | Ebay Inc. | Search guidance |
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CN113222018B (zh) * | 2021-05-13 | 2022-06-28 | 郑州大学 | 一种图像分类方法 |
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