A NEW CONTENT BASED IMAGE RETRIEVAL SYSTEM USING GMM AND RELEVANCE FEEDBACK

Abstract

Content-Based Image Retrieval (CBIR) is also known as Query By Image Content (QBIC) is the application of computer vision techniques and it gives solution to the image retrieval problem such as searching digital images in large databases. The need to have a versatile and general purpose Content Based Image Retrieval (CBIR) system for a very large image database has attracted focus of many researchers of information-technology-giants and leading academic institutions for development of CBIR techniques. Due to the development of network and multimedia technologies, users are not fulfilled by the traditional information retrieval techniques. So nowadays the Content Based Image Retrieval (CBIR) are becoming a source of exact and fast retrieval. Texture and color are the important features of Content Based Image Retrieval Systems. In the proposed method, images can be retrieved using color-based, texture-based and color and texture-based. Algorithms such as auto color correlogram and correlation for extracting color based images, Gaussian mixture models for extracting texture based images. In this study, Query point movement is used as a relevance feedback technique for Content Based Image Retrieval systems. Thus the proposed method achieves better performance and accuracy in retrieving images.