An Automatic Relevance Feedback in Image Retrieval Using Belief Functions

This paper proposes an automatic relevance feedback approach for content-based image retrieval using information fusion and without any user input. This method is proposed as an alternative of the simple ranking of result images. The idea consists to pass from a simple user selected query image to multi-images query in order to get more information about the query image type. Given a query image, the system first computes its feature vector to rank the images according to a well-chosen similarity measure. For each retrieved image, the degree of belief about the relevance is then assigned as a function of this measure. This degree of belief is then updated using an iterative process. At each iteration, we evaluate, for each retrieved image, the degree of relevance using the combination of belief functions associated to previously retrieved images. Then, each retrieved image is not found by the query image only but it is found by the query image and previously retrieved images too. Some experimental results will be proposed in this paper in order to demonstrate that the methodology improves the efficiency and accuracy of retrieval systems.