A novel Bayesian framework for relevance feedback in image content-based retrieval systems

This paper presents a new algorithm for image retrieval in content-based image retrieval systems. The objective of these systems is to get the images which are as similar as possible to a user query from those contained in the global image database without using textual annotations attached to the images. The main problem in obtaining a robust and effective retrieval is the gap between the low level descriptors that can be automatically extracted from the images and the user intention. The algorithm proposed here to address this problem is based on the modeling of user preferences as a probability distribution on the image space. Following a Bayesian methodology, this distribution is the prior distribution and its parameters are modified based on the information provided by the user. This yields the a posteriori from which the predictive distribution is calculated and used to show to the user a new set of images until he/she is satisfied or the target image has been found. Experimental results are shown to evaluate the method on a large image database in terms of precision and recall.