A Method of Image Retrieval Based on Maximum Similarity Matching

A technology of maximum similarity and picture, applied in still image data retrieval, still image data indexing, special data processing applications, etc., can solve problems such as affecting accuracy, and achieve the effect of improving accuracy, eliminating multiple matching, and high computing efficiency

Active Publication Date: 2018-08-24
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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  • A Method of Image Retrieval Based on Maximum Similarity Matching
  • A Method of Image Retrieval Based on Maximum Similarity Matching
  • A Method of Image Retrieval 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 retrieval method based on maximum similarity matching, comprising the following steps: 1) acquiring a training picture set; 2) performing feature point detection and description on the acquired pictures in a multi-scale space; 3) performing step 2) The extracted feature set is clustered and a visual dictionary containing k visual words is generated; 4) Each feature extracted in step 2) is mapped to the visual word with the smallest distance from the current feature l2, and the current feature is compared with the corresponding visual word The normalized residual vector of is stored in the inverted index structure to form a query database; 5) Obtain the image to be retrieved, perform steps 2) and 4), and obtain the inverted index structure of the image to be retrieved, according to the inverted index structure Retrieve the query database, and obtain the retrieval results of the images to be retrieved based on the maximum similarity matching. Compared with the prior art, the present invention has the advantages of good robustness, high calculation 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 Patents(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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