An Adaptive Approach to Relevance Feedback in CBIR Using Mining Techniques

ISBN 978-93-82338-22-2 | © 2012 Bonfring Abstract--This paper provides a mining approach to the research area of relevance feedback (RF) in contentbased image retrieval (CBIR). Relevance feedback is a powerful technique in CBIR systems, in order to improve the performance of CBIR effectively. The drawbacks in CBIR are the features of the query image and the semantic gap between low-level features and high level concepts. Especially, Mining Image data is the one of the essential features in this present scenario since image data plays vital role in every aspect of the system such as business for marketing, hospital for surgery, engineering for construction, Web for publication and so on. In this paper, we are proposed an adaptive approach for relevance feedback in CBIR using mining techniques. Where in the processes of feedback we are using a new technique called Image retrieval based on optimum clusters is proposed for improving user interaction with image retrieval systems by fully exploiting the similarity information. The index is created by describing the images according to their color characteristics, with compact feature vectors, that represent typical color distributions.