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

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 ...

Images