Relevance feedback techniques in the MARS image retrieval system

Content-based image retrieval (CBIR) has become one of the most active research areas in the past few years. Many visual feature representations have been explored and many systems built. In this paper, we focus on an important component of these systems - relevance feedback - and how we incorporated it into the MARS retrieval system. Relevance feedback techniques are based on an interactive retrieval approach to effectively take into account user preferences to provide an improved search experience. We present a series of coherent strategies, from single-point to multipoint and multifeature approaches that we have seamlessly integrated into our system and present experimental results to show their retrieval performance characteristics.Keywords: Image retrieval - Query refinement - Relevance feedbackMichael Ortega-Binderberger: michaelo@us.ibm.comThis work was performed while the author was a Ph.D. student at the University of Illinois at Urbana-Champaign. Correspondence to: This material is based on work supported in part by the National Science Foundation under Award Numbers CAREER IIS-9734300, 9996140, 0083489, 0331707, and 0331690 and in part by the Army Research Laboratory under Cooperative Agreement No. DAAL01-96-2-0003. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation or the Army Research Laboratory. Michael Ortega-Binderberger was supported in part by CONACYT award # 89061.