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Maximum average entropy-based scale-invariant?feature?transform (SIFT) descriptor binaryzation and similarity matching method

A similarity matching and binarization technology, applied in the field of image matching, can solve problems such as the huge amount of face image data, and achieve the effects of simple and fast calculation, reduced complexity, and good real-time performance.

Active Publication Date: 2014-03-05
BEIJING UNIV OF TECH
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Problems solved by technology

However, the amount of data to represent face images with SIFT is huge

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  • Maximum average entropy-based scale-invariant?feature?transform (SIFT) descriptor binaryzation and similarity matching method
  • Maximum average entropy-based scale-invariant?feature?transform (SIFT) descriptor binaryzation and similarity matching method
  • Maximum average entropy-based scale-invariant?feature?transform (SIFT) descriptor binaryzation and similarity matching method

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[0031] The overall process of the technical solution of the present invention is as follows in the description attached figure 1shown. Our method greatly reduces the amount of data storage, reduces the complexity of calculation, and can better meet the requirements of real-time performance. And the matching result equivalent to the original SIFT descriptor can be obtained, which is far better than the result of uniform binarization of the SIFT descriptor for matching.

[0032] A. For each face image, first extract the SIFT feature descriptor, and then binarize the SIFT descriptor to obtain the information entropy of each level, according to the probability of occurrence of 0 and 1 in each layer and the total number of bits reserved , to obtain the bit average entropy, and then find out the number of binarization layers corresponding to the maximum bit average entropy, keep the binarization results of these layers, and finally form a new binarization descriptor. Specific step...

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Abstract

The invention relates to a maximum average entropy-based scale-invariant?feature?transform (SIFT) descriptor binaryzation and similarity matching method and belongs to the image matching field. Scale-invariant?feature?transform (SIFT) operators have strong matching ability, while, the scale-invariant?feature?transform (SIFT) operators will bring a huge amount of data, and therefore, binaryzation should be performed on the scale-invariant?feature?transform (SIFT) operators, however, if unified binaryzation is performed on all the operators, data redundancy or information loss will be brought about. The maximum average entropy-based scale-invariant?feature?transform (SIFT) descriptor binaryzation and similarity matching method of the invention comprises the following steps that: binaryzation is performed on the scale-invariant?feature?transform (SIFT) operators; average entropy calculation is performed on each layer of binaryzation results, such that different numbers of binaryzation layers are selected adaptively; new binaryzation descriptors are provided; the Hamming distance is utilized to replace the Euclidean distance so as to calculate the distance between two descriptors; and the distance between the two descriptors is compared with a set threshold value. With the maximum average entropy-based scale-invariant?feature?transform (SIFT) descriptor binaryzation and similarity matching method of the invention adopted, information of original features is reserved; the amount of data storage can be greatly reduced; computational complexity can be reduced; a requirement for a real-time property can be realized better; the matching results equivalent to original scale-invariant?feature?transform (SIFT) descriptors can be obtained and are far superior to results of matching by using the unified binaryzation.

Description

technical field [0001] The invention relates to the technical field of image matching, in particular to a SIFT descriptor binarization method based on maximum bit average entropy and a similarity matching scheme thereof. Background technique [0002] With the development of modern computer technology, face recognition technology has been widely used in security authentication, human-computer communication, public security system, etc., and also plays a great role in video conferencing, file management, medical treatment, etc. After the 911 terrorist attacks in the United States and the leakage of network CSDN user information, biometric technology has attracted more attention, and the recognition of facial biometrics has always been a hot spot in the field of biometrics. Under normal circumstances, good recognition performance can be obtained, but in practical applications, face recognition is often affected by many factors. When the face posture changes, the expression chan...

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Application Information

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IPC IPC(8): G06K9/64G06K9/38G06K9/00
Inventor 毋立芳侯亚希周鹏许晓曹航明颜凤辉曹瑜漆薇
Owner BEIJING UNIV OF TECH
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