A New Approach for CBIR Feedback based Image Classifier

Recent years have seen a rapid increase in the size of digital image collections. This ever increasing amount of multimedia data creates a need for new sophisticated methods to retrieve the information one is looking for. The classical approach alone cannot keep up with the rapid growth of available data anymore. Thus content-based image retrieval attracted many researchers of various fields. There exist many systems for image retrieval meanwhile. Retrieval of Images from Image archive using Suitable features extracted from the content of Image is currently an active research area. The CBIR problem is identified because there is a need to retrieve the huge databases having images efficiently and effectively. For the purpose of content-based image retrieval (CBIR) an up-todate 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 CBIR with interactive user feedback based image classification by Using Suitable 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.