Using Co-occurrence and Granulometry Features for Content Based Image Retrieval

Abstract

This communication presents a novel system for Content Based Image Retrieval (CBIR) using Granulometry and Color Co-occurrence Features (CCF). These features are extracted directly from images using visual codebook. Relative distance measures are used to identify the similarity between the stored images and the query image. Results show that proposed method of using Granulometry and CCF is superior to most state of the art CBIR systems. The proposed system is tested on Wang image database that contains 1000 images having different categories. The performance of the system, quantified using the Average Precision Rate (APR), is very encouraging.