Accurate Image Retrieval Algorithm Based on Color and Texture Feature

Content-Based Image Retrieval (CBIR) is one of the most active hot spots in the current research field of multimedia retrieval. According to the description and extraction of visual content (feature) of the image, CBIR aims to find images that contain specified content (feature) in the image database. In this paper, several key technologies of CBIR, e. g. the extraction of the color and texture features of the image, as well as the similarity measures are investigated. On the basis of the theoretical research, an image retrieval system based on color and texture features is designed. In this system, the Weighted Color Feature based on HSV space is adopted as a color feature vector, four features of the Co-occurrence Matrix, saying Energy, Entropy, Inertia Quadrature and Correlation, are used to construct texture vectors, and the Euclidean distance for similarity measure is employed as well. Experimental results show that this CBIR system is efficient in image retrieval.