JP2014102772A - Program, device, and method for calculating similarity between contents represented by sets of feature vectors - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a program, device, and method which are capable of accurately calculating similarity between a query content and an object reference content.SOLUTION: The device includes: reference feature vector search means which acquires a set of reference feature vectors from reference feature vector storage means with respect to each query feature vector of a query content and searches for N (N>1) reference feature vectors in the descending order of similarity; object reference feature vector search means which searches for an object reference feature vector having the highest similarity in an object reference content; score calculation means which calculates scores on the basis of a ranking showing the position where a distance between the query feature vector and the object reference feature vector having the highest similarity is placed in ranking of distances between the query feature vector and the N reference feature vectors; and similarity calculation means which calculates, as similarity, the sum total of scores of all query feature vectors.

Description

  The present invention relates to a technique for calculating a similarity between contents represented by a set of feature vectors. In addition, by calculating the similarity between contents, query content (content serving as a search key) also represented by a set of feature vectors from a set of reference contents (contents to be searched) represented by a set of feature vectors ) Related to a technology for searching for reference contents similar to those with high accuracy. In particular, it is suitable for searching multimedia contents (for example, images) represented by a set of high-dimensional feature vectors.

  In recent years, not limited to online / offline, it has become possible to accumulate a large amount of content as the capacity of the storage increases. In addition, with the widespread use of information terminal devices typified by mobile phones and smartphones, digital content such as photograph data acquired by the user can be easily stored in a large amount in a database. Offline databases include storage devices such as HDD (Hard Disk Drive), DVD (Digital Versatile Disk), and Blu-ray disc. Online databases include social network services such as Flickr (registered trademark) and MySpace (registered trademark). According to these storage devices and services, a technique for searching for a large amount and various multimedia contents of individuals stored in a database becomes important.

  In order to search for multimedia contents, there is a technique for extracting a large number of feature vectors from these contents and outputting contents having a high degree of similarity between sets of feature vectors as search results. According to this technique, feature vectors of multimedia content are quantized and a histogram is created from the frequency of the quantized feature vectors. The similarity (distance) is calculated by the distance of the L1 norm or L2 norm between the histograms. The norm represents the distance between two points. The L1 norm means the sum of the absolute values of the dimensions of the two points, and the L2 norm means the sum of the squares of the values of the two points.

  There is also a technique for extracting a large amount of local feature vectors from image content, vector quantizing them, and calculating the similarity based on the number of local feature vectors vector-quantized to the same representative vector (for example, Non-Patent Document 1). reference).

  Furthermore, there is a technique for extracting a plurality of local invariant feature amounts from an image, making a histogram of the frequency of feature vectors, and calculating the similarity between the image and the category based on the overlapping ratio of the histograms (see, for example, Patent Document 1). . According to this technique, features (for example, background features) that are not necessary for pattern recognition of a subject can be removed based on the histogram. As a result, the feature of the object can be extracted without previously separating the object and the non-object from the image.

  Conventionally, a similar image search framework using local features is referred to as “Bag-of-Visual Words” (or Bag-of-Features, Bag-of-Keypoints) (see, for example, Non-Patent Document 1). According to this technique, a sentence retrieval method using a Bag-of-Words model and a transposed index is applied to retrieval of similar images. Bag-of-Words expresses a sentence as a feature vector defined by the frequency of one word, and uses IDF (Inverse Document Frequency) derived in advance based on the sentence set to determine the similarity between sentences. It is a framework to derive. On the other hand, Bag-of-Visual Words quantizes the local feature quantity of an image, regards the local feature quantity after quantization as a word, and expresses it as one feature vector similarly defined by the frequency. The same analogy method can be applied using the weighting used.

  In some cases, it may be desirable to further improve the accuracy of the list of reference feature vectors arranged in descending order of similarity to the query feature vectors. Further, there is a case where it is desired to determine whether or not the same object is included in the query image and the reference image using a threshold value based on the similarity. For these cases, there is a reranking technique for calculating a more accurate score for the top N reference feature vectors obtained from the first search result (see, for example, Patent Document 7).

  FIG. 1 is an image correspondence diagram for calculating the similarity between the query content and the target reference content in the prior art.

  According to FIG. 1, based on the ratio (d1 / d2) between the distance d1 to the nearest target reference feature vector and the distance d2 to the second nearest target reference feature vector as seen from the query feature vector, The matching process is executed. The matching process is repeated until the distance ratio becomes equal to or less than a predetermined threshold value. Finally, when the distance ratio is equal to or less than a predetermined threshold, the score is calculated with a similarity of +1.

JP 2010-282581 A JP 2009-020769A

J. Sivic et al., "Video Google: A Text Retrieval Approach to Object Matching in Videos," in Proc. ICCV, 2003. H. Jegou, M. Douze, and C. Schmid, "Improving bag-offeatures for large scale image search," in IJCV, vol.87, no.3, pp.316-336, 2010. Y. Uchida, M. Agrawal, and S. Sakazawa, "Accurate Content-Based Video Copy Detection with Efficient Feature Indexing," in Proc. Of ICMR, 2011. D. G. Lowe, "Distinctive Image Features from Scale-InvariantKeypoints," International Journal of Computer Vision, vol. 60, no. 2, pp.91-110, 2004. H. Jegou, M. Douze, and C. Schmid, "Product quantization fornearest neighbor search," in IEEE Trans. On PAMI, vol. 33, no. 1, pp117-128, 2011. O. Boiman, E. Shechtman, and M. Irani, "In defense of nearest-neighbor based image classification," in Proc. Of CVPR, 2008. J. Philbin, O. Chum, M. Isard, J. Sivic, and A. Zisserman, "Object Retrieval with Large Vocabularies and Fast Spatial Matching," in Proc of CVPR, 2007. O. Chum and J. Matas, "Matching with PROSAC-ProgressiveSample Consensus," in Proc. Of CVPR, 2005.

  However, according to the existing Bag-of-Visual Words technique, IDF in sentence search is used when calculating a score of similarity between contents based on a feature vector. IDF represents an index for identifying how characteristic a specific word that appears in a sentence is for the purpose of text mining. In the case of IDF, it is designed on the premise that “each sentence is searched by a small number of words included in the sentence”, such as proper nouns. In other words, it is a premise that “each sentence is not searched by a word not included in the sentence”. Specifically, the IDF is calculated as the characteristic degree of the word in the corpus from the number of times a specific word appears in the sentence and the natural logarithm of the number of sentences including the sentence in the entire corpus. Is done.

  On the other hand, in the case of image search, it is necessary to extract a high-dimensional feature vector from a local invariant feature region. For example, according to a typical SIFT (Scale-Invariant Feature Transform) for extracting a feature vector used for object recognition, a feature region is divided into a plurality of blocks, and the direction of the luminance gradient from each block is used as a weighted histogram. Extract.

  Here, the query content may include many feature vectors unrelated to the object depicted in the reference content. Specifically, this is a case where an image obtained by photographing the object with a camera is used as query content. In the reference content, for example, the background is white and only the search target object is shown, whereas in the query content, not only the target object but also various objects appear in the background. That is, various feature vectors irrelevant to the target object are detected in the background of the query content. This causes a decrease in search accuracy.

  In addition, even if an attempt is made to calculate an accurate similarity for the top N cases using the reranking technique, the distance ratio (d1 / d2) is 1 when there are a plurality of substantially identical feature portions in the reference image. . That is, even if it is a correct corresponding point (feature vector), it may not be determined that matching has been performed, and there is a problem that the accuracy of matching depends on the content of the reference image. There is also a problem that the influence of the number of reference feature vectors in the reference image cannot be normalized.

  Therefore, according to the present invention, it is possible to accurately calculate the similarity between the query content and the target reference content based on the distribution of distances between the plurality of reference feature vectors as seen from the query feature vector. It is an object to provide a program, an apparatus, and a method that can be used.

According to the present invention, there is provided a similarity calculation program for causing a computer mounted on an apparatus to function in order to calculate a similarity between a query content represented by a set of feature vectors and a target reference content R.
Reference feature vector storage means for storing each feature vector extracted from a large number of reference contents R _j ;
Reference feature vector search means for acquiring a set of reference feature vectors from the reference feature vector storage means for each query feature vector of the query content, and searching for the top N (> 1) reference feature vectors in order from the most similar ,
For each query feature vector of the query content, target reference feature vector search means for searching for the most similar target reference feature vector in the target reference content,
For each query feature vector of the query content, based on the ranking of the distance from the most similar target reference feature vector to the top N in the distance to each of the top N reference feature vectors Score calculating means for calculating a score;
The computer is caused to function as a similarity calculation unit that uses a sum of scores of all query feature vectors in the query content as a similarity.

According to another embodiment of the similarity calculation program of the present invention,
The score calculation means determines the order of the distance between the most similar target reference feature vector and the distance to each of the top N reference feature vectors.
If it is within the top N, score 1
It is also preferable to make the computer function so that the score is 0 when it is not within the top N cases.

According to another embodiment of the similarity calculation program of the present invention,
The score calculation means calculates the score by the following formula when the rank order of the distance from the most similar target reference feature vector to the distance from each of the top N reference feature vectors corresponds to the K-th place. S i = log {λ / (1-λ) · | Rall | / (K · | R |) +1}
λ: Adjustable parameter
| R |: Number of feature vectors in the target reference content
| Rall |: accumulated in the reference feature vector accumulation means
It is also preferred to have the computer function as the number of reference feature vectors.

According to another embodiment of the similarity calculation program of the present invention,
The similarity calculation means obtains a correspondence relationship between the query feature vectors of the query content and the most similar target reference feature vector in the target reference content, and from the set of correspondence relationships, a homography between the query content and the target reference content It is also preferable that the computer function so that a matrix is calculated and only the scores for the feature vectors of the query content having a correspondence relationship satisfying the homogprahy matrix are summed.

According to another embodiment of the similarity calculation program of the present invention,
The degree-of-similarity calculation means calculates the Homography matrix using each of the query feature vectors of the query content in order from the correspondence relationship with the highest score among the correspondence relationships with the most similar target reference feature vector in the target reference content. It is also preferable to make the computer function.

According to the present invention, using the above-described similarity calculation program, a search program for causing a computer installed in the apparatus to function so as to search for reference content similar to query content from a set of reference content,
The reference feature vector storage means stores a reference content identifier in association with each reference feature vector extracted from a plurality of reference contents R _j ,
Similar vector search means for searching M (> 1) similar reference feature vectors for each query feature vector q _i of the query content using the reference feature vector storage means;
Using the mixed parameter λ, each query feature vector q _{i of} the query content is generated from the probability λ · p (q _i | R _j ) generated from each searched reference content and a background model unrelated to the reference content. Based on the probability ratio with the generated probability (1-λ) · p (q _i ), adding a score for each reference content R _{j is} performed for all feature vectors q _i of the query content, And finally, voting means for outputting, as a search result, reference content R _j that has obtained a higher score above a predetermined threshold,
The similarity calculation program causes the computer to function so as to rearrange the order again by calculating the similarity between each reference content item R _j output by the voting means and each query feature vector of the query content. It is characterized by.

According to another embodiment of the search program of the present invention,
The voting means calculates the score s _j of the reference content j with respect to the feature vector q _i of the query content by the following formula: s _j = log {λ / (1-λ) · | R _all | / (a · | R _j |) +1}
a: Feature vector of reference content j is among the top M
Number that appears for the first time
| R _all |: Number of all feature vectors in all reference contents
| R _j |: Number of all feature vectors in the reference content j
λ, 1-λ: It is also preferable to make the computer function like a mixed parameter.

According to another embodiment of the search program of the present invention,
The query content and the reference content are images,
It is also preferable that at least one instance (object, object) belonging to the same object or the same category is shown in the image as the reference content.

According to the present invention, there is provided a similarity calculation device for calculating a similarity between a query content represented by a set of feature vectors and a target reference content R,
Reference feature vector storage means for storing each feature vector extracted from a large number of reference contents R _j ;
Reference feature vector search means for acquiring a set of reference feature vectors from the reference feature vector storage means for each query feature vector of the query content, and searching for the top N (> 1) reference feature vectors in order from the most similar ,
For each query feature vector of the query content, target reference feature vector search means for searching for the most similar target reference feature vector in the target reference content,
For each query feature vector of the query content, based on the ranking of the distance from the most similar target reference feature vector to the top N in the distance to each of the top N reference feature vectors Score calculating means for calculating a score;
It is characterized by having similarity calculation means for making the sum of the scores of all the query feature vectors in the query content a similarity.

According to the present invention, the search device includes all the functions of the above-described similarity calculation device, and searches for reference content similar to query content from a set of reference content,
The reference feature vector storage means stores a reference content identifier in association with each reference feature vector extracted from a plurality of reference contents R _j ,
Similar vector search means for searching at least one set D of similar reference feature vectors for each query feature vector q _i of the query content using reference feature vector storage means;
Using the mixed parameter λ, each query feature vector q _{i of} the query content is generated from the probability λ · p (q _i | R _j ) generated from each searched reference content and a background model unrelated to the reference content. Based on the probability ratio with the generated probability (1-λ) · p (q _i ), adding a score for each reference content R _{j is} performed for all feature vectors q _i of the query content, And finally, voting means for outputting, as a search result, reference content R _j that has obtained a higher score above a predetermined threshold,
The similarity calculation program rearranges the order again by calculating the similarity between each reference content item R _j output by the voting means and each query feature vector of the query content.

According to the present invention, there is provided a similarity calculation method in an apparatus for calculating a similarity between a query content represented by a set of feature vectors and a target reference content R,
A reference feature vector storage unit storing each feature vector extracted from a large number of reference contents R _j ;
For each query feature vector of query content, a first step of acquiring a set of reference feature vectors from a reference feature vector storage unit and searching for the top N (> 1) reference feature vectors in order from the most similar ones;
A second step of searching for the most similar target reference feature vector in the target reference content for each query feature vector of the query content;
For each query feature vector of the query content, based on the ranking of the distance from the most similar target reference feature vector to the top N in the distance to each of the top N reference feature vectors A third step of calculating a score;
And a fourth step in which a sum of scores of all query feature vectors in the query content is used as a similarity.

According to the present invention, there is provided a search method in an apparatus for searching for reference content similar to query content from a set of reference content, including all steps of the above-described similarity calculation method,
The reference feature vector storage unit stores a reference content identifier in association with each reference feature vector extracted from a plurality of reference contents R _j ,
As the first step of the first step,
A first step of searching for at least one set D of similar reference feature vectors for each query feature vector q _i of the query content using reference feature vector storage means;
Using the mixed parameter λ, each query feature vector q _{i of} the query content is generated from the probability λ · p (q _i | R _j ) generated from each searched reference content and a background model unrelated to the reference content. Based on the probability ratio with the generated probability (1-λ) · p (q _i ), adding a score for each reference content R _{j is} performed for all feature vectors q _i of the query content, And finally, a reference content R _j that obtains an upper score equal to or higher than a predetermined threshold is output as a search result.
By executing the first to fourth steps, the similarity between the query content vectors of the query content is calculated for each reference content file R _j output in the 02th step, and the ranks are arranged again. It is characterized by changing.

  According to the program, apparatus, and method of the present invention, when viewed from the query feature vector, the similarity between the query content and the target reference content is accurately determined based on the distribution of distances between the plurality of reference feature vectors. Can be calculated.

It is an image corresponding | compatible figure which calculates the similarity between the query content and target reference content in a prior art. It is a functional block diagram of the similarity calculation apparatus in this invention. It is a 1st image correspondence diagram showing calculation of the score in this invention. It is a 2nd image correspondence diagram showing calculation of the score in this invention. It is a 3rd image corresponding figure showing calculation of the score in this invention. It is an image corresponding | compatible figure showing the matching between the query feature vector and object reference feature vector added as a score of query content. It is an image correspondence figure showing inlier and outlier based on a Homography matrix. It is a functional block diagram of the search device in this invention. It is explanatory drawing which votes from the several feature vector of several reference content.

Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. In particular, the description will be divided into two embodiments as follows.
<Calculation of similarity between query content and target reference content>
<Search for reference content similar to query content>

<Calculation of similarity between query content and target reference content>
The present invention basically relates to a program, an apparatus, and a method for calculating a similarity between query content represented by a set of feature vectors and target reference content.

  FIG. 2 is a functional configuration diagram of the similarity calculation apparatus according to the present invention.

  According to FIG. 2, the similarity calculation device 1 inputs query content and target reference content, and calculates the similarity between the two contents. The query content and the reference content are images, for example. In this case, the image as the reference content includes at least one instance (object, object) belonging to the same object or the same category.

  The similarity calculation device 1 includes a reference feature vector accumulation unit 10, a feature vector set extraction unit 11, a reference feature vector search unit 12, a target reference feature vector search unit 13, a score calculation unit 14, and a similarity calculation unit. 15. These functional components are realized by executing a program that causes a computer installed in the apparatus to function. The processing order of these functions can also be understood as a similarity calculation method.

[Reference Feature Vector Accumulator 10]
The reference feature vector storage unit 10 stores each feature vector extracted from a large number of reference contents.

[Feature vector set extraction unit 11]
The feature vector set extraction unit 11 extracts a set of feature vectors from one multimedia content. For example, when the multimedia content is an image, the feature vector is a local feature vector extracted from the local feature region of the image.

Specifically, the feature vector set extraction unit 11 extracts a set Q (= {q _i }) of query feature vectors from the query content, and the set Q of query feature vectors includes the reference feature vector search unit 12 and the target This is output to the reference feature vector search unit 13.
Further, the feature vector set extraction unit 11 extracts a set R (= {R _j }) of target reference feature vectors from the target reference content, and outputs the set R of reference feature vectors to the target reference feature vector search unit 13. Is done. Note that the query feature vector of the query content and the target reference feature vector of the target reference content have the same number of dimensions.

  For example, SIFT or SURF (Speeded Up Robust Features) is used as an algorithm for extracting a feature vector used for object recognition. For example, in the case of SIFT, a set of 128-dimensional feature vectors is extracted from one image (for example, see Non-Patent Document 4). SIFT is a technique for analyzing a characteristic local region using a scale space and describing a feature vector that is invariant to scale change and rotation. On the other hand, in the case of SURF, higher-speed processing is possible than SIFT, and a set of 64-dimensional feature vectors is extracted from one image.

[Reference Feature Vector Search Unit 12]
The reference feature vector search unit 12 refers to the reference feature vector storage unit 10 for each query feature vector of the query content, and searches the top N (> 1) reference feature vectors in order from the most similar. The searched N reference feature vectors are output to the score calculation unit 14.

  FIG. 3 is a first image correspondence diagram showing calculation of scores in the present invention.

According to FIG. 3, the top N = 8 reference feature vectors similar to the query feature vector q _i are surrounded by a dashed circle. That is, the top N = 8 reference feature vectors surrounded by a broken-line circle are searched.

[Target Reference Feature Vector Search Unit 13]
The target reference feature vector search unit 13 searches for the most similar target reference feature vector in the target reference content for each query feature vector of the query content. According to FIG. 3, the target reference feature vector rj that is most similar to the query feature vector q _i is represented by x. The searched most similar target reference feature vector is output to the score calculation unit 14.

[Score calculator 14]
For each query feature vector q _i of the query content, the score calculation unit 14 determines that the distance d between the most similar target reference feature vector r _j and the top N reference feature vectors is the distance d _1- The score s _i is calculated based on the rank (= K) from the top in d _N.

As the simplest first method, the score calculation unit 14 ranks the distance N between the most similar target reference feature vectors with respect to the distance to each of the top N reference feature vectors. A score s _i is given depending on whether it is within the range. For example:
If it is within the top N (d1 ≦ d ≦ d _N )-> score s _i = 1
If not in top N (d _N <d)-> score s _i = 0

As another second method, the score calculating unit 14 corresponds to the rank of the distance from the most similar target reference feature vector with respect to the distance to each of the top N reference feature vectors. In this case, the score may be calculated by the following formula.
s _i = log {λ / (1-λ) · | Rall | / (K · | R |) +1}
λ: Adjustable parameter
| R |: Number of feature vectors in the target reference content
| Rall |: accumulated in the reference feature vector accumulation unit
Number of reference feature vectors However, if d _N <d, the score s _i = 0.

According to FIG. 3, the distance d between the query feature vector q _i and the most similar target reference feature vector r _j is the distances d _{1 to} d ₈ between the top N = 8 reference feature vectors. Corresponds to the highest order (K = 1). The distance between the query feature vector and each reference feature vector has a relationship such as d <d ₁ . Based on this ranking, a score is calculated by the following formula.
First method: s _i = 1
Second method: s _i = log {λ / (1-λ) · | Rall | / (1 · | R |) +1}

  FIG. 4 is a second image correspondence diagram showing score calculation in the present invention.

According to FIG. 4, the distance d between the query feature vector q _i and the most similar target reference feature vector r _j is the distances d _{1 to} d ₈ between the top N = 8 reference feature vectors. Corresponds to the fourth place from the top (K = 4). The distance between the query feature vector and each reference feature vector has a relationship of d ₃ <d <d ₄ . Based on this ranking, a score is calculated by the following formula.
First method: s _i = 1
Second method: s _i = log {λ / (1-λ) · | Rall | / (4 · | R |) +1}

  FIG. 5 is a third image correspondence diagram showing calculation of scores in the present invention.

According to FIG. 5, the distance d between the query feature vector q _i and the most similar target reference feature vector r _j is the distances d _{1 to} d ₈ between the top N = 8 reference feature vectors. It corresponds to the 9th place from the top. The distance between the query feature vector and each reference feature vector has a relationship of d ₈ <d. Thus, when it is not within the top N = 8 cases (d _N <d), the score s _i is as follows.
First method: s _i = 0
Second method: s _i = 0

[Similarity calculation unit 15]
The similarity calculation unit 15 sets the total score of all query feature vectors in the query content as the similarity.

  FIG. 6 is an image correspondence diagram showing matching between the query feature vector and the target reference feature vector, which is added as the query content score.

  Here, as another embodiment, it is also preferable to add only a specific score without simply making the sum of the scores of all the query feature vectors the similarity. Specifically, a Homography matrix is calculated between the query content and the reference content, and only matching that satisfies the matrix is added to the score.

  First, the similarity calculation unit 15 obtains a correspondence relationship between each query feature vector of the query content and the most similar target reference feature vector in the target reference content. This is obtained from the matching result between the query feature vector and the target reference feature vector in the score calculation unit 14.

Next, the similarity calculation unit 15 calculates a Homography matrix between the query content and the target reference content from the set of correspondence relationships. As the similarity between the query content and the target reference content is higher, the feature vectors are linearly projected geometrically. Therefore, the coordinates can be replaced by the Homography matrix H which is a planar projective transformation matrix. The Homography matrix H is expressed as follows.

The number of unknown parameters in the Homography matrix H is 8 (h _{0 to} h ₇ ), and one set of corresponding points gives two equations. Therefore, if there are four or more pairs of corresponding points, this matrix can be calculated by the least square method. That is, four sets are selected at random from the matching results, and a Homography matrix is calculated from the four sets.

When the similarity calculation unit 15 projects each point of the query content using the homography matrix, the determination is made as follows.
(1) If the reference feature vector that is the matching result is projected close to a predetermined threshold value or less, it is determined as inlier.
(2) Conversely, if it is projected farther than the predetermined threshold, it is determined as outlier.
After this process is executed multiple times, only the Homography matrix with the largest number of inliers is adopted.

  FIG. 7 is an image correspondence diagram showing inlier and outlier based on the Homography matrix. outlier is represented by a broken line.

In addition, as a conventional technique, when calculating Homography, a technique has been proposed in which not all matching is used at once, but highly reliable matching is preferentially used (see, for example, Non-Reference Document 8). . According to this technique, the distance between the query feature vector and the reference feature vector is used as the reliability. However, since the density of feature vectors is not uniform, a simple distance is not appropriate as an index of reliability. On the other hand, according to the present invention, high accuracy is realized by using the score s _i as the reliability and calculating the Homography matrix preferentially using matching with high s _i .

  Finally, the similarity calculation unit 15 sums only the scores for the feature vectors of the query content having a correspondence relationship that satisfies the homogprahy matrix.

  Also, the similarity calculator 15 does not add outlier matching to the score, so that highly accurate matching can be obtained. As is clear from FIG. 6, only the scores corresponding to matching results that are consistent with the Homography matrix (= inlier) are summed.

Normally, according to the conventional technique, the number of matching that becomes inlier is used as a score, but in the present invention, a score with high reliability is calculated by introducing a score s _i into each matching that becomes inlier. can do. That is, according to the present invention, the sum of the matching scores s _i is used.

  Further, as another embodiment, the similarity calculation unit 15 uses a correspondence that has a high score among the correspondence relationships between the query feature vector of the query content and the most similar target reference feature vector in the target reference content. It may be calculated using the relations in order.

<Search for reference content similar to query content>
The present invention can also realize a program, an apparatus, and a method for searching for reference content similar to query content from a large amount of reference content using the similarity calculation function described above.

  FIG. 8 is a functional configuration diagram of the search device according to the present invention.

  The search device 2 inputs a large number of reference contents in advance, and stores information related to the reference contents in the reference feature vector storage unit 10. In addition, the search device 2 inputs query content serving as a search key at the time of search, and uses the reference feature vector storage unit 10 to search for reference content most similar to the query content.

  According to FIG. 8, the search device 2 includes all the functions of the similarity calculation device of FIG. 2, and further includes a similar vector search unit 21 and a voting unit 22. These functional components are realized by executing a program that causes a computer installed in the apparatus to function. The processing order of these functions can also be understood as a search method.

[Feature vector set extraction unit 11]
The feature vector set extraction unit 11 extracts a set of feature vectors for each reference content, and outputs the set of feature vectors to the reference feature vector storage unit 10. The feature vector set extraction unit 11 also extracts a feature vector set Q (= {q _i }) from the query content, and outputs the feature vector set to the similar vector search unit 21. The feature vector of the reference content and the feature vector of the query content have the same number of dimensions.

[Reference Feature Vector Accumulator 10]
Reference feature vectors storage unit 10 in association with the respective feature vectors extracted from a plurality of reference content R _j, and stores the reference content identifier.

In the following embodiment, a case where a set of feature vectors R _j is extracted from a plurality of reference contents j will be described in detail. On the other hand, as in Non-Patent Document 6, for example, the query content can be classified into categories by setting the feature vector set R _j as a set of feature vectors based on a specific category. In this case, as will be described later, a score for each category can be calculated for the query content and classified into a plurality of top categories having the highest score, or a tag of a category having a score of a certain level or more can be added. .

[Similar vector search unit 21]
Using the reference feature vector storage unit 10, the similar vector search unit 21 searches the feature vector q _i of the reference content having a similar feature vector for each feature vector q _i of the query content. The shorter the distance between the query content feature vector q _i and the reference content feature vector, the higher the similarity. Specifically, a method using Cartesian product quantization (for example, see Non-Patent Document 5) or a method using Hamming Embedding (for example, see Non-Patent Document 2), which is one of the Approximate Nearest Neighbor algorithms. It is also preferable to use LSH (Locality-Sensitive Hashing). The reference content ID based on the searched set D of one or more feature vectors is output to the voting unit 22.

[Voting unit 22]
The voting unit 22 executes addition of scores for each reference content R _j for all feature vectors q _i of the query content, and finally, the reference content R _j that has obtained a higher score above a predetermined threshold value. Is output as a search result. According to the present invention, the voting unit 22 is voted by the calculation formula described in detail below, whereas the voting unit 22 has voted by the IDF according to the prior art.

According to the present invention, the reference content j ′ that is most likely to have generated the query content is derived. The following equation represents the posterior probability and represents the probability p that the query content would have been generated from the jth reference content.
j ′ = argmax _j p (R _j | Q)
Q: Set of feature vectors of query content R _j : Set of feature vectors of j-th reference content p (R _j | Q): From set Q of feature vectors of query content
A posteriori probability that a set of reference content feature vectors R _j is generated
argmax _j : means to derive j that maximizes the posterior probability of the right term

In general, the following posterior probability formula is established using Bayes' theorem. In this method, the posterior probability is calculated by multiplying the prior probability by the likelihood probability.
j ′ = argmax _j p (R _j | Q) = argmax _j p (Q | R _j ) p (R _j )
p (Q | R _j ): From the set R _j of reference content feature vectors,
Likelihood probability p (R _j ) that a set Q of query content feature vectors is generated: A set Rj of reference content feature vectors is searched
Prior probability
(The higher p (R _j ) means the higher the probability of being searched)

Here, it is assumed that the reference content to be searched is not biased and p (R _j ) is constant regardless of j. Then, p (R _j ) can be deleted, and is simply expressed as follows.
j ′ = argmax _j p (Q | R _j )

Here, it is assumed that the set Q of query content feature vectors is generated independently. “Independently generated” means that when a certain feature appears, there is no influence that a specific feature appears next, that is, it does not affect the previous result. In this case, it is the product of the probabilities based on the individual feature vectors q ₁ , q ₂ , q ₃ ,. In this case, it is represented by the following formula.
j ′ = argmax _j Π _{i = 1} ⁿ p (q _i | R _j )

Furthermore, the product of the probabilities can generally be expressed by the sum Σ of logs. This is because the magnitude relationship of the probabilities is maintained because it is a monotonically increasing function. In this case, it is represented by the following formula.
j ′ = argmax _j Π _{i = 1} ⁿ p (q _i | R _j ) = argmax _j Σ _{i = 1} ⁿ log p (q _i | R _j )

Here, each query feature vector is modeled as a linear combination of the probability generated from the feature vector set of the reference content and the probability generated from the background model unrelated to the reference content.
j ′ = argmax _j Σ _{i = 1} ⁿ logp (q _i | R _j )
= Argmax _j Σ _{i = 1} ⁿ log (λp (q _i | R _j ) + (1-λ) · p (q _i ))
= Argmax _j Σ _{i = 1} ⁿ (log (λp (q _i | R _j ) + (1-λ) · p (q _i )) − log (1-λ) · p (q _i ))
= Argmax _j Σ _{i = 1} ⁿ log {λ / (1-λ) · p (q _i | R _j ) / p (q _i ) +1}
i: ID of feature vector of query content
λ: Mixed parameter of linear combination p (q _i ): Probability generated from background model unrelated to reference content
(Based on the background image unrelated to the object in the query content)
λp (q _i | R _j ) + (1-λ) · p (q _i ):
The sum of the probability of p (q _i | R _j ) at λ and the probability of p (q _i ) at (1-λ) is
-Log (1-λ) · p (q _i ) which means the overall probability:
Even if an overall penalty is drawn for deformation, the order does not change.
For transformation of the formula described later.
λ / (1-λ) · p (q _i | R _j ) / p (q _i ) +1:
Based on "loga-logb = loga / b"

According to the present invention, the probability λ · p (q _i | R _j ) that each feature vector q _i of the query content is generated from each searched reference content using the mixed parameter λ, the reference content, The probability ratio with the probability (1-λ) · p (q _i ) generated from an irrelevant background model is used.

Here, it puts like the following formula.
s _ij = log {λ / (1-λ) · p (q _i | R _j ) / p (q _i ) +1}
i: ID of feature vector of query content
j: ID of the reference content
q _i : Feature vector of query content R _j : Reference content s _ij means that when query content feature vector q _i is observed, each q _i means a score for obtaining reference content j . That is, when q _i is observed, each means the likelihood generated from the reference content j.

Then, “s _ij ” is calculated for all of the feature vector i and the reference content j of the query content. Then, the reference content R _j having the maximum score Σ _{i = 1} ⁿ s _ij is selected as a search result.

However, since s _ij must be calculated for all i for each i, the amount of calculation becomes enormous when targeting a large-scale database.

Therefore, according to the present invention, an approximation method is applied to extract the top A (≧ 1) of feature vectors similar to q _i from the feature vector set of reference content for the query content feature vector q _i. To do. Then, the calculation of s _ij is approximated as follows. “S _ij ” is calculated only for the reference content R _j including the feature vector corresponding to the upper A cases. For other R _j , p (q _i | R _j ) = 0. At this time, s _ij = Since log (1) = 0, the score of the reference content that does not include the feature vector corresponding to the top A is not increased or decreased.

Then, using the top A feature vectors, p (q _i | R _j ) and p (q _i ) are calculated by the k-neighbor density estimation method.
p (q _i | R _j ) = 1 / (| R _j | · V _a )
p (q _i ) = a / (| Rall | · V _a )
Rall: Feature vector set of all reference content a: Feature vector of reference content j in top A
Number that appears for the first time V _a : A hypersphere whose radius is the distance between q _i and the a-th feature vector (three or more dimensions)
Volume (in 2 dimensions, area, 1 dimension represents length)
Here, p (q _i ) is obtained by dividing the probability of a (of k neighborhood density estimation method) falling out of | Rall | (N in k neighborhood density estimation method) by volume V _a. Represents something.

Here, when calculating V _a , a large amount of calculation is required. Therefore, according to the present invention, the term of V _a is deleted by substituting it into the expression of s _ij , and is calculated by the following expression.
s _ij = log {λ / (1-λ) · (| Rall | / a · | R _j |) +1}

That is, the voting unit 22 of the present invention calculates the score s _j of the reference content j with respect to the feature vector q _i of the query content for the feature vectors from the top to the A th by the following formula.
s _j = log {λ / (1-λ) · (| R _all | / a · | R _j |) +1)}
a: Feature vector of reference content j is among the top A
Number that appears for the first time
| R _all |: Number of all feature vectors in all reference contents
| R _j |: Number of all feature vectors in the reference content j
λ, 1-λ: Mixing parameters

[Specific Processing Contents in Similar Vector Search Unit 21 and Voting Unit 22]
Below, the specific processing content in the similar vector search part 21 and the voting part 22 in this invention is explained in full detail.

At the time of search, a set Q of feature vectors is extracted from the query content, and for each feature vector q _i , the top A feature vectors of similar reference content are searched. Then, vote for the corresponding reference content ID. After voting for all the feature vectors q _i of the query content, a reference content ID having a higher score is used as a search result.

  FIG. 9 is an explanatory diagram for voting from a plurality of feature vectors of a plurality of reference contents.

  According to FIG. 9, from each feature vector of the query content, using a method using direct product quantization, a method using Hamming Embedding (see Non-Patent Document 2, for example), an algorithm such as LSH, etc. The neighborhood feature vector is searched.

(First feature vector, a = 1st)
Reference content ID = 1:
score ₁ = score ₁ + log {λ / (1-λ) · (| Rall | / 1 | R ₁ |) +1}
(Second feature vector, a = 2nd)
Reference content ID = 4:
score ₄ = score ₄ + log {λ / (1-λ) · (| Rall | / 2 | R ₁ |) +1}
(Third feature vector, a = third)
Reference content ID = 1:
* Score ₁ = score ₁ + log {λ / (1-λ) · (| Rall | / 3 | R ₁ |) +1}
★ Reference content ID = 1 is not the first feature vector that appears, so it will not be used in the score.
(Fourth feature vector, a = 4th)
Reference content ID = 5:
score ₅ = score ₅ + log {λ / (1-λ) · (| Rall | / 4 | R ₁ |) +1}
(Fifth feature vector, a = 5th)
Reference content ID = 4:
score ₄ = score ₄ + log {λ / (1-λ) · (| Rall | / 5 | R ₁ |) +1}
★ Reference content ID = 4 is not used for the score because it is not the first feature vector.
(Sixth feature vector, a = 6th)
Reference content ID = 1:
score ₁ = score ₁ + log {λ / (1-λ) · (| Rall | / 6 | R ₁ |) +1}
★ Reference content ID = 1 is not the first feature vector that appears, so it will not be used in the score.
(Seventh feature vector, a = seventh)
Reference content ID = 6:
score ₆ = score ₆ + log {λ / (1-λ) · (| Rall | / 7 | R ₁ |) +1}
(Eighth feature vector, a = 8th)
Reference content ID = 6:
score ₆ = score ₆ + log {λ / (1-λ) · (| Rall | / 8 | R ₁ |) +1}
★ Reference content ID = 6 is not used for the score because it is not the first feature vector.

  According to FIG. 8, the list of reference IDs that are the voting results calculated by the voting unit 22 is input to the similarity calculation program of the present invention. The similarity calculation program can calculate the similarity between the query content and each reference content, and can output the reference content with the highest similarity as a search result.

  As described above in detail, according to the program, apparatus, and method of the present invention, the query content and the target reference content are based on the distribution of distances between the plurality of reference feature vectors when viewed from the query feature vector. Can be calculated accurately.

  Various changes, modifications, and omissions of the above-described various embodiments of the present invention can be easily made by those skilled in the art. The above description is merely an example, and is not intended to be restrictive. The invention is limited only as defined in the following claims and the equivalents thereto.

DESCRIPTION OF SYMBOLS 1 Similarity calculation apparatus 10 Reference feature vector storage part 11 Feature vector set extraction part 12 Reference feature vector search part 13 Target reference feature vector search part 14 Score calculation part 15 Similarity calculation part 2 Search apparatus 21 Similar vector search part 22 Voting part

Claims (12)

A similarity calculation program for causing a computer mounted on a device to function in order to calculate the similarity between query content represented by a set of feature vectors and target reference content R,
Reference feature vector storage means for storing each feature vector extracted from a large number of reference contents R _j ;
For each query feature vector of query content, a reference feature vector search unit that acquires a set of reference feature vectors from the reference feature vector storage unit and searches for the top N (> 1) reference feature vectors in order from the most similar When,
For each query feature vector of the query content, target reference feature vector search means for searching for the most similar target reference feature vector in the target reference content,
For each query feature vector of the query content, based on the ranking of the distance from the most similar target reference feature vector to the top N in the distance to each of the top N reference feature vectors Score calculating means for calculating a score;
A similarity calculation program that causes a computer to function as a similarity calculation unit that uses a sum of scores of all query feature vectors in query content as a similarity. The score calculation means is configured such that the ranks of the distances to the most similar target reference feature vectors with respect to the distances to the top N reference feature vectors are:
If it is within the top N, score 1
The similarity calculation program according to claim 1, wherein the computer is caused to function so as to obtain a score of 0 when it is not within the top N cases. The score calculation means, when the rank of the distance to the most similar target reference feature vector with respect to the distance to each of the top N reference feature vectors corresponds to the K rank, the score is calculated using the following formula: S i = log {λ / (1-λ) · | Rall | / (K · | R |) +1}
λ: Adjustable parameter
| R |: Number of feature vectors in the target reference content
| Rall |: stored in the reference feature vector storage means
The similarity calculation program according to claim 1, wherein the computer is caused to function as the number of reference feature vectors.   The similarity calculating unit obtains a correspondence relationship between the query feature vectors of the query content and the most similar target reference feature vector in the target reference content, and determines the correspondence between the query content and the target reference content from the set of correspondence relationships. The similarity calculation program according to claim 3, wherein the computer is caused to function so as to calculate a Homography matrix and sum only scores for feature vectors of query contents having a correspondence relationship satisfying the Homogprahy matrix.   The similarity calculation means calculates the Homography matrix using each of the query feature vectors of the query content in order from the correspondence with the highest score among the correspondences with the most similar target reference feature vector in the target reference content. The similarity calculation program according to claim 4, wherein the computer is caused to function. A search that causes a computer installed in the apparatus to function so as to search for reference content similar to query content from a set of reference content using the similarity calculation program according to any one of claims 1 to 5. A program,
The reference feature vector storage means stores a reference content identifier in association with each reference feature vector extracted from a plurality of reference contents R _j ,
Similar vector search means for searching M (> 1) similar reference feature vectors for each query feature vector q _i of the query content using the reference feature vector storage means;
Using the mixed parameter λ, each query feature vector q _{i of} the query content is generated from the probability λ · p (q _i | R _j ) generated from each searched reference content and a background model unrelated to the reference content. Based on the probability ratio with the generated probability (1-λ) · p (q _i ), adding a score for each reference content R _{j is} performed for all feature vectors q _i of the query content, And finally, voting means for outputting, as a search result, reference content R _j that has obtained a higher score above a predetermined threshold,
The similarity calculation program calculates a similarity between each reference content item R _j output by the voting means and each query feature vector of the query content, thereby rearranging the order again. A search program characterized by functioning. The voting means calculates a score s _j of the reference content j with respect to the feature vector q _i of the query content by the following formula: s _j = log {λ / (1-λ) · | R _all | / (a · | R _j |) +1}
a: Feature vector of reference content j is among the top M
Number that appears for the first time
| R _all |: Number of all feature vectors in all reference contents
| R _j |: Number of all feature vectors in the reference content j
The search program according to claim 6, wherein the computer is caused to function as λ, 1-λ: mixed parameters. The query content and the reference content are images,
The search program according to any one of claims 1 to 7, wherein at least one instance (object, object) belonging to the same object or the same category is shown in the image as the reference content. . A similarity calculation device for calculating a similarity between a query content represented by a set of feature vectors and a target reference content R,
Reference feature vector storage means for storing each feature vector extracted from a large number of reference contents R _j ;
For each query feature vector of query content, a reference feature vector search unit that acquires a set of reference feature vectors from the reference feature vector storage unit and searches for the top N (> 1) reference feature vectors in order from the most similar When,
For each query feature vector of the query content, target reference feature vector search means for searching for the most similar target reference feature vector in the target reference content,
For each query feature vector of the query content, based on the ranking of the distance from the most similar target reference feature vector to the top N in the distance to each of the top N reference feature vectors Score calculating means for calculating a score;
A similarity calculation device comprising: similarity calculation means for calculating a sum of scores of all query feature vectors in query content. A search device that includes all the functions of the similarity calculation device according to claim 9 and searches for reference content similar to the query content from a set of reference content,
The reference feature vector storage means stores a reference content identifier in association with each reference feature vector extracted from a plurality of reference contents R _j ,
Similar vector search means for searching at least one set D of similar reference feature vectors for each query feature vector q _i of query content using the reference feature vector storage means;
Using the mixed parameter λ, each query feature vector q _{i of} the query content is generated from the probability λ · p (q _i | R _j ) generated from each searched reference content and a background model unrelated to the reference content. Based on the probability ratio with the generated probability (1-λ) · p (q _i ), adding a score for each reference content R _{j is} performed for all feature vectors q _i of the query content, And finally, voting means for outputting, as a search result, reference content R _j that has obtained a higher score above a predetermined threshold,
The similarity calculation program rearranges the order again by calculating a similarity between each reference content item R _j output by the voting means and each query feature vector of the query content. Search device to do. A similarity calculation method in an apparatus for calculating a similarity between a query content represented by a set of feature vectors and a target reference content R,
A reference feature vector storage unit storing each feature vector extracted from a large number of reference contents R _j ;
A first step of acquiring a set of reference feature vectors from the reference feature vector storage unit for each query feature vector of query content and searching for the top N (> 1) reference feature vectors in order from the most similar one; ,
A second step of searching for the most similar target reference feature vector in the target reference content for each query feature vector of the query content;
For each query feature vector of the query content, based on the ranking of the distance from the most similar target reference feature vector to the top N in the distance to each of the top N reference feature vectors A third step of calculating a score;
A similarity calculation method, comprising: a fourth step in which a sum of scores of all query feature vectors in the query content is used as a similarity. A search method in an apparatus for searching for reference content similar to the query content from a set of reference content, including all steps of the similarity calculation method according to claim 11,
The reference feature vector storage unit stores a reference content identifier in association with each reference feature vector extracted from a plurality of reference contents R _j ,
As the first step of the first step,
A first step of searching for at least one set D of similar reference feature vectors for each query feature vector q _i of query content using the reference feature vector storage means;
Using the mixed parameter λ, each query feature vector q _{i of the} query content is generated from each searched reference content λ · p (q _i | R _j ) and a background model unrelated to the reference content Based on the probability ratio with the probability (1-λ) · p (q _i ) generated from, the score is added for each reference content R _j for all feature vectors q _i of the query content And finally, a reference content R _j that has obtained a higher score above a predetermined threshold is output as a search result, and a second step is further provided.
By executing the first to fourth steps, the similarity between each query content vector of the query content is calculated for each reference content item R _j output in the 02nd step, and the ranking is again determined. A search method characterized by sorting.

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