Image Retrieval Relevance Feedback Algorithms:Trends and Techniques

With many applications, Content based Image Retrieval (CBIR) has come into the attention in recent decades. To reduce the schematic gap a wide variety of relevance feedback (RF) algorithms have been developed in recent years to improve the performance of CBIR systems. These RF algorithms capture user’s preferences and bridge the semantic gap. 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, different RF techniques are reviewed. The selection of papers include sources from image processing journals, conferences, books, dissertations and thesis out of more than 500 journals, books and online research databases. The state of art research on each category is provided with emphasis on developed technologies and image properties used by them. Finally, conclusions are drawn summarizing commonly used techniques and their complexities in applications.