Design & performance analysis of content based image retrieval system based on image classification usingvarious feature sets

With the rapid advancement of digital imaging technologies, and the use of large volume image databases in various applications, it becomes imperative to build an automatic and an efficient image retrieval system. Content Based Image Retrieval (CBIR) is most emerging and vivid research area in computer vision, in which unknown query image assigns to the closest possible similar images available in the database. Current systems mainly use colour, texture, and shape information for image retrieval using similarity measures between query and database images features. Here this work, proposed a classification system that allows recognizing and recovering the class of a query image based on its visual content. This successful categorization of images greatly enhances the performance of retrieval by filtering out irrelevant classes. In this way we have done the comparative analysis of various features as an individual or in combinations, with direct similarity measure and proposed framework. Experiments on benchmark Wang database show that the proposed classification & retrieval framework performs significantly better than the common framework of distances.