Concept Content Based Retrieval Performance using Texture Analytics

Abstract

Image retrieval is a poor stepchild to other forms of information retrieval (IR). Image retrieval has been one of the most interesting and research areas in the field of computer vision over the last few decades. Content-Based Image Retrieval (CBIR) systems are used in order to automatically index, search, retrieve, and browse image databases. Color and texture analytics are important properties in content-based image retrieval systems. In this paper we have mentioned detailed classification of CBIR system. We have defined different techniques as well as the combinations of them to improve the performance. We have also defined the effect of different matching techniques on the retrieval process. Most content-based image retrievals (CBIR) use color as image analytics. However, image retrieval using color analytics often gives disappointing results because in many cases, images with similar colors do not have similar content. Color methods incorporating spatial information have been proposed to solve this problem; however, these methods often result in very high dimensions of analytics which drastically slow down the retrieval speed. In this paper, a method combining both color and texture analytics of image is proposed to improve the retrieval performance. Given a query, images in the database are firstly ranked using color analytics. Then the top ranked images are re-ranked according to their texture analytics.