Comparative Evaluation of Image Retrieval Algorithms using Relevance Feedback and it's Applications

Abstract

Now adays, content-based image retrieval (CBIR) is the mainstay of image retrieval systems. To be more profitable, Relevance Feedback (RF) techniques were incorporated into CBIR such that more precise results can be obtained by taking user's feedbacks into account. In this paper Content Based Image Retrieval algorithms using Relevance Feedback technique are discussed. The comparative study of these algorithms is done. This article covers various techniques for implementing Content Based Image Retrieval algorithms , their evaluation parameters used and various possible applications of Content Based Image Retrieval algorithms .

Content Based Retrieval Systems in a Clinical Context

“…While CBIR systems should operate in a transparent manner, in order to increase their overall accuracy it can be desirable to allow a user to relate back to the system which results are actually relevant. Relevance feedback is the process of automatically adjusting an image query using the information provided from the expert on previously executed queries [20]. A way to achieve this goal is to expose to the user an interface that allows him to provide feedback on the relevancy of the results on a per-image basis.…”