Vector-quantization-based overall and local color image searching method

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

Active Publication Date: 2013-12-11
CHONGQING UNIV OF POSTS & TELECOMM
View PDF2 Cites 31 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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 distrib

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Vector-quantization-based overall and local color image searching method
  • Vector-quantization-based overall and local color image searching method
  • Vector-quantization-based overall and local color image searching method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0022] 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.

[0023] 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:

[0024] Color space quantization: select HSV space as the color quantization space, and convert RGB space to HSV space...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

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 (Text Based 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 ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
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
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap