Based On Color And Texture Features Of Image Retrieval Research

At present,Content-Based Image Retrieval(CBIR) is becoming a hot research topic.lt is a retrieval technology based on the vision features.such as the color.texture and shape of the image.lt is different from traditional retrieval technology based on the text.This technology directly analyzes image content and extracts image features. These contental features to be built index and be used in retrieval.The main techniques of CBIR are discussed in this thesis. During the study of color features extraction and matching, HSV color model is chosen and divided into small spaces according to the perception of human eyes. The details of extracting HSV-based color histogram are described. In order to deal with the shortcoming of color histogram - it includes no spatial distribution information of the image, this thesis proposes a new content-based color image retrieval method, in which both the color and the spatial relationship of image are taken into account and then designs 12 overlapped sub-regions to get the color accumulative histogram in each region.The new method divides the image into blocks, which contain spatial information. The color feature based quantification,texture feature based Co-occurrence matrix are selected in feature retrieval.In multi-features retrieval,the feedback will be used in adjusting weight and re-used in multi-features retrieval.The methods for image retrieval using color and texture features are first discussed. On the basis of using color and texture features separately, a new method for image retrieval using combined color and texture feature is proposed. Retrienal experiments using real color image database are carried out.The results show that the retrieval results obtained from combined-features fits more closely with human perception than the retrieval results obtained from single-features.