Analysis of Relevance Feedback in Content Based Image Retrieval

Relevance feedback in content based image retrieval (CBIR) has been an active field of research for quite some time now. Many schemes and techniques of relevance feedback exist with many assumptions and operating criteria. Yet there exist few ways of quantitatively measuring and comparing different relevance feedback algorithms. Such analysis is necessary if a CBIR system is to perform consistently. In this paper we propose an abstract model of a CBIR system where the effects of different modules over the entire system is observed. Using this model we thoroughly analyse performance a set of basic relevance feedback algorithms. Besides using standard measures like precision and recall we also suggest two new measures to gauge the performance of any contemporary CBIR system