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A Global and Local Color Image Retrieval Method Based on Vector Quantization

A technology of vector quantization and color indexing, applied in image analysis, image data processing, special data processing applications, etc., can solve the problems of inaccurate color space quantization, insufficient description of color space distribution, and lack of prominent local important information.

Active Publication Date: 2016-11-02
CHONGQING UNIV OF POSTS & TELECOMM
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AI Technical Summary

Problems solved by technology

[0007] The present invention aims at the problems of inaccurate color space quantification, insufficient description of color spatial distribution and inconspicuous local important information in the existing color image retrieval method, and proposes a color image retrieval method

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  • A Global and Local Color Image Retrieval Method Based on Vector Quantization
  • A Global and Local Color Image Retrieval Method Based on Vector Quantization
  • A Global and Local Color Image Retrieval Method Based on Vector Quantization

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Embodiment Construction

[0020] The meanings of the variables used in the following are as follows: X represents the training vector; Y represents the codebook; ω represents the final codebook synthesized by the three color components; N represents the size of the codebook; H represents the color index histogram; D represents the main color transfer matrix ; P represents the precision rate.

[0021] The invention starts from the color space quantization and the local interest area, improves the color quantization precision, fully reflects the color distribution of the image and enhances the local features of the image to improve the retrieval performance. The present invention will be further described below using specific examples and accompanying drawings, figure 2 It is the basic flowchart of the method of the present invention. The specific implementation steps are as follows:

[0022] Color space quantization: select HSV space as the color quantization space, and convert RGB space to HSV space...

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Abstract

The invention relates to a vector-quantization-based overall and local color image searching method. The invention provides a new color image searching method, and relates to the field of an image processing technology. The method comprises the steps of converting an RGB (red, green and Blue) color space into an HSV (Hue, Saturation, Value) color space, performing relatively accurate clustering division on the color space by applying a neural-network-based competitive learning algorithm training code book; describing a space distribution situation of colors by introducing a color transfer matrix; combining the two characteristics, namely an index column diagram and a main color transfer matrix, so as to perform the similarity measurement; processing images by applying a morphological opening-and-closing operation, highlighting a target contour so as to extract a local interest area so as to highlight an important area and limit background information. The color image searching method overcomes the defects that the color space distribution description is not enough and the background information cannot be effectively limited by using the overall color histogram method. By using the vector-quantization-based overall and local color image searching method, the color quantization is relatively accurate, the matching effect is relatively good, and the vector-quantization-based overall and local color image searching method is an effective method for further improving the researching efficiency.

Description

technical field [0001] The invention belongs to the field of content-based image retrieval, in particular to a color retrieval method based on the combination of global and local regions of interest based on vector quantization. Background technique [0002] With the development of computer technology, multimedia technology and network technology, a large amount of image data is widely disseminated through the Internet. However, due to the lack of effective image retrieval methods, the utilization of huge image databases has been extremely inefficient. There are usually three methods for image data retrieval: free browsing, text-based image retrieval (TextBased Image Retrieval, TBIR) and content-based image retrieval (Content Based Image Retrieval, CBIR). Free browsing is only suitable for occasional situations, and it is not suitable for professional customers who often use special multimedia information. There are two problems in text-based image retrieval: one is that i...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/30G06T7/40
Inventor 陈善学于佳佳李俊韩勇冯银波
Owner CHONGQING UNIV OF POSTS & TELECOMM
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