(untitled) (International Journal of Engineering and Computer Science, 2017)
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.