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Target retrieval method based on group of randomized visual vocabularies and context semantic information

A visual dictionary and semantic information technology, applied in computer components, secure communication devices, character and pattern recognition, etc., can solve problems such as high computational complexity, achieve enhanced differentiation, reduce semantic gap, and solve computational complexity Effect

Inactive Publication Date: 2012-09-26
THE PLA INFORMATION ENG UNIV
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Problems solved by technology

[0007] Aiming at the deficiencies of the existing technologies, the present invention proposes a target retrieval method based on randomized visual dictionary groups and contextual semantic information, which effectively solves the high computational complexity caused by multiple iterations of traditional clustering algorithms and query expansion techniques, And better reduce the semantic gap between the manually defined target area and the user's retrieval intention, and enhance the differentiation of the target

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  • Target retrieval method based on group of randomized visual vocabularies and context semantic information
  • Target retrieval method based on group of randomized visual vocabularies and context semantic information
  • Target retrieval method based on group of randomized visual vocabularies and context semantic information

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Embodiment 1

[0071] Embodiment 1: This embodiment is based on the target retrieval method of randomized visual lexicon group and contextual semantic information. First, aiming at the low efficiency of traditional clustering algorithms and the problem of synonymous and ambiguous visual words, E 2 LSH clusters the local feature points of the training image database to generate a set of randomized visual dictionaries that support dynamic expansion; secondly, select the query image and use a rectangular frame to define the target area, and then extract the query image and the image database according to Lowe's method SIFT features and perform E on them 2 LSH mapping realizes the matching of feature points and visual words; then, on the basis of the language model, uses the rectangular frame area and image saliency detection to calculate the retrieval score of each visual word in the query image and obtain the target model containing the target context semantic information ;Finally, for the pro...

Embodiment 2

[0073] Embodiment two: see figure 2 , image 3 , Figure 4 , the target retrieval method based on the randomized visual dictionary group and contextual semantic information of this embodiment adopts the following steps to generate 2 LSH's randomized visual dictionary set:

[0074] For each hash function g i (i=1,...,L), use it to perform hash mapping on the SIFT points of the training image library, and the points with very close distances in the space will be stored in the same bucket of the hash table, with the center of each bucket represents a visual word, each function g i can generate a hash table, a visual dictionary. Then, L functions g 1 ,..., g L can generate a visual dictionary group, the process is as follows figure 2 shown.

[0075] Among them, the detailed process of single visual dictionary generation can be described as follows:

[0076] (1) SIFT feature extraction of the training image library. In this paper, Oxford5K, a commonly used database for...

Embodiment 3

[0103] Embodiment 3: The difference between this embodiment and Embodiment 2 is that the following steps are used to measure the similarity:

[0104] The similarity between the query image q and any image d in the image database can be measured by the query likelihood p(q|d), then:

[0105] p ( q | d ) = Π i = 1 M q p ( q i | d ) - - - ( 14 )

[0106] Transforming this into a risk minimization problem, that is, given a query image q, the risk function for returning an image d is defined as follows:

[0107]

[0108]

[0109] p(θ D |d)p(r|θ Q ,θ D )dθ Q dθ D ...

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Abstract

The invention relates to a target retrieval method based on a group of randomized visual vocabularies and context semantic information. The target retrieval method includes the following steps of clustering local features of a training image library by an exact Euclidean locality sensitive hash function to obtain a group of dynamically scalable randomized visual vocabularies; selecting an inquired image, bordering an target area with a rectangular frame, extracting SIFT (scale invariant feature transform) features of the inquired image and an image database, and subjecting the SIFT features to S<2>LSH (exact Euclidean locality sensitive hashing) mapping to realize the matching between feature points and the visual vocabularies; utilizing the inquired target area and definition of peripheral vision units to calculate a retrieval score of each visual vocabulary in the inquired image and construct an target model with target context semantic information on the basis of a linguistic model; storing a feature vector of the image library to be an index document, and measuring similarity of a linguistic model of the target and a linguistic model of any image in the image library by introducing a K-L divergence to the index document and obtaining a retrieval result.

Description

technical field [0001] The invention relates to a target retrieval method based on a randomized visual dictionary group and contextual semantic information. Background technique [0002] In recent years, with the rapid development and application of computer vision, especially image local features (such as SIFT) and visual dictionary method (BoVW, Bag of Visual Words), the target retrieval technology has become more and more practical, and has been obtained in real-life products. widely used. For example, Tineye is a network-oriented near-duplicate image retrieval system, and Google Goggles allows users to use mobile phones to take pictures and retrieve information related to the objects contained in the pictures. The BoVW method is inspired by the word set method in the field of text retrieval. Due to its outstanding performance, the BoVW method has become the mainstream method in the field of target retrieval, but it also has some open problems. One is the low time effic...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06K9/62
CPCH04L9/3236
Inventor 赵永威李弼程高毫林蔺博宇
Owner THE PLA INFORMATION ENG UNIV
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