Extraction of image features for an effective CBIR system

In this paper, we propose a content-based image retrieval system based on an efficient combination of both color and texture features. According to HSV (Hue, Saturation, and Value) color space, we quantified the color space into non-equal intervals, and then construct a one dimensional feature vector and represented the color feature. Similarly, the work of texture feature extraction is obtained by using Gray level co-occurrence matrix (GLCM) or Color co-occurrence matrix (CCM) and then we combine color features and GLCM as well as CCM separately. Depending on the former, image retrieval based on multi-feature fusion is achieved by using normalized Euclidean distance classifier. Experiments reveal that the use of both color and texture based on CCM has better effective performance and advantage.