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Picture searching method based on maximum similarity matching

A technology of maximum similarity and pictures, which is applied in still image data retrieval, still image data indexing, special data processing applications, etc., can solve problems affecting accuracy, achieve improved accuracy, high computing efficiency, and enhance visual matching Effect

Active Publication Date: 2015-05-13
TONGJI UNIV
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AI Technical Summary

Problems solved by technology

[0006] Due to the approximate nature of the non-aggregate model, the multiple matching problem inevitably occurs in the visual matching process, thus affecting the final accuracy

Method used

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  • Picture searching method based on maximum similarity matching
  • Picture searching method based on maximum similarity matching
  • Picture searching method based on maximum similarity matching

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Embodiment Construction

[0041] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is carried out on the premise of the technical solution of the present invention, and detailed implementation and specific operation process are given, but the protection scope of the present invention is not limited to the following embodiments.

[0042] An embodiment of the present invention provides an image retrieval method based on maximum similarity matching, including an offline training step and an online retrieval step. Such as figure 1 As shown, the offline training steps include:

[0043] Step s101: Obtain a training picture set.

[0044] Step s102: Use the improved version of Hessian-Affine feature point detection algorithm and SIFT local feature descriptor to detect and describe feature points in multi-scale space, specifically:

[0045] 1a) Use the Hessian-Affine feature point detection algorithm to detect the p...

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Abstract

The invention relates to a picture searching method based on maximum similarity matching. The method includes the following steps that (1) a training picture set is acquired; (2) feature point detection and description are conducted on acquired pictures in a multi-scale space; (3) feature sets extracted in the second step are clustered and generated into a visual dictionary including k visual vocabularies; (4) each feature extracted in the second step is mapped to the visual vocabulary with the distance being smallest to the current feature l2, the current feature and the normalization residual vector of the corresponding visual vocabulary are stored in a reverse index structure, and accordingly a query database is formed; (5) the pictures to be searched for are acquired, the second step and the fourth step are executed again, the reverse index structure of the pictures to be searched for is acquired, the query database is searched for according to the reverse index structure, and the searching results of the pictures to be searched for are acquired based on the maximum similarity matching. Compared with the prior art, the picture searching method has the advantages of being good in robustness, high in computational efficiency and the like.

Description

technical field [0001] The invention relates to a method for retrieving similar pictures, in particular to a method for retrieving pictures based on maximum similarity matching. Background technique [0002] Computer vision has developed rapidly in recent years, especially image retrieval, which has attracted much attention due to its rich application scenarios. [0003] Image local features are a type of features used in the field of image processing. Finding extreme points in the scale space, extracting position, scale, and rotation invariants can detect key points in the image. [0004] Non-aggregated models are an approximate method for feature matching. In this model, the local features are quantized to the visual words in the pre-trained dictionary closest to it, and the residual vectors of this feature and the corresponding visual words are stored and put into the inverted index for query. [0005] Nowadays, the image retrieval system based on local features and non...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06K9/66
CPCG06F16/51G06F16/5866G06F18/2321
Inventor 王瀚漓王雷朱冯贶天
Owner TONGJI UNIV
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