There is a huge demand for the efficient content based image retrieval system because of the


availability of large image databases. In this paper we have present an efficient CBIR framework by


extracting the Dominant-color, Texture, edge features and by clustering feature database. We have applied


the dominant color extraction using color-quantization technique. Initially the image is divided into some


partitions using the color quantization algorithm, here we are dividing into eight partitions and the eight


dominant colors are obtained from that partition. Next for shape feature extraction sobel color edge


detection technique is used. And local binary pattern (LBP) is performed on gray scale image to extract the


texture feature. Then all features discussed above of image are combined to form a single feature vector. Kmeans


clustering is applied over combined feature vector of database images. Finally, to retrieve similar


images from database similarity matching is performed by Euclidian distance which compares feature vector


of clustered database images with that of query image. The result of this proposed approach provides


efficient, more accurate result.