Image retrieval method based on hierarchical features and genetic programming relevance feedback

A technology of genetic programming and correlation feedback, applied in image analysis, image data processing, special data processing applications, etc., can solve the problem that the retrieval mode is not suitable for segmentation results, etc.

Inactive Publication Date: 2013-07-17
HENAN UNIVERSITY
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Problems solved by technology

However, they are global feature-based correlation feedback and uniformly segmented region-based correlation feedback respectively, and the proposed retrieval mode is not suitable for general segmentation results

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  • Image retrieval method based on hierarchical features and genetic programming relevance feedback
  • Image retrieval method based on hierarchical features and genetic programming relevance feedback
  • Image retrieval method based on hierarchical features and genetic programming relevance feedback

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Embodiment Construction

[0042] Such as figure 1 As shown, an image retrieval method based on hierarchical features and genetic programming related feedback disclosed by the present invention specifically includes the following steps:

[0043] (1) Retrieve images submitted by users Carry out adaptive segmentation to obtain the segmented area ;

[0044] Mean Shift (Mean Shift, MS) and Normalized Cuts (Normalized Cuts, NC) are two commonly used image segmentation methods, but MS is prone to over-segmentation, and the computational complexity of NC is too high. Combining MS and NC, Wenbin Tao et al. proposed a new image segmentation method, MS-Ncut, which combines MS and NC, which alleviates over-segmentation and computational complexity to a certain extent. The image is segmented; and then the area is merged with the normal cut method on the basis of the over-segmented image obtained in the previous step. But the MS-Ncut method needs to pre-set the number of splits to end the merging process. ...

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Abstract

The invention discloses an image retrieval method based on hierarchical features and genetic programming relevance feedback. The method includes the steps: (1), performing adaptive segmentation for a retrieval image submitted by a user to obtain segmented regions; (2) extracting global features of the retrieval image, and extracting local low-level features of the segmented regions; (3) computing an optimal region, corresponding to each segmented region, of each image in a standard image library; (4) constructing a global-optimal region similarity matched pattern; (5) by attaching average weight to various similarities in the similarity matched pattern, computing similarity of the retrieval image to each image in the standard image library, sorting according to the similarity to obtain an initial retrieval result, and returning a plurality of previous images most similar to the retrieval image of the user to a user side; and (6) enabling the user to participate in feedback until satisfactory images are retrieved. The method can be closer to retrieval intention of the user, and is capable of extracting content features of the image effectively and completing retrieval quickly and effectively.

Description

technical field [0001] The invention relates to the field of image retrieval, in particular to an image retrieval method based on hierarchical features and genetic programming correlation feedback. Background technique [0002] At present, with the rapid development of multimedia and Internet technology, people are exposed to more and more various information. As a kind of multimedia information with rich content and intuitive expression, image has been favored by people for a long time. How to quickly and effectively search for the information you need? In the 1990s, Content Based Image Retrieval (CBIR) appeared, which discussed image retrieval from the perspective of visualization. The so-called CBIR is to represent the image content by extracting the underlying features of the image, such as color, texture and other features, and the matching between images is the matching of image features. [0003] The description of image content includes global descriptors and local...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30G06T7/00
Inventor 李登峰杨晓慧朱秀阁彭李超刘占卫吴国昌蔡利君
Owner HENAN UNIVERSITY
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