Image Retrieval with Interactive Relevance Feedback Based Classification by Using Kernel Based Classifier

With advances in the computer technology and the World Wide Web there has been an explosion in the amount and complexity of multimedia data that are generated, stored, transmitted, analyzed, and accessed.. This ever increasing amount of multimedia data creates a need for new stylish methods to get back the information one is looking for. Thus content-based image retrieval attracted many researchers of various fields. Retrieval of Images from Image library using appropriate features extracted from the content of Image is currently an active research area. For the intention of content-based image retrieval (CBIR) an up-to-date comparison of state-of-the-art low-level color and texture feature extraction approach is discussed. In this paper we propose A New Approach for Image Retrieval with interactive Relevance feedback based classification by Using Kernel Based Classifier .This Approach is applied to improve retrieval performance. Our aim is to select the most informative images with respect to the query image by ranking the retrieved images. This approach uses suitable feedback to repeatedly train the Histogram Intersection Kernel based Classifier. Proposed Approach retrieves mostly informative and correlated images.