Learning from User Feedback in Image Retrieval Systems

Abstract

We formulate the problem of retrieving images from visual databases as a problem of Bayesian inference. This leads to natural and effective solutions for two of the most challenging issues in the design of a retrieval system: providing support for region-based queries without requiring prior image segmentation, and accounting for user-feedback during a retrieval session. We present a new learning algorithm that relies on belief propagation to account for both positive and negative examples of the user's interests. 1 Introduction Due to the large amounts of imagery that can now be accessed and managed via computers, the problem of content-based image retrieval (CBIR) has recently attracted significant interest among the vision community [1, 2, 5]. Unlike most traditional vision applications, very few assumptions about the content of the images to be analyzed are allowable in the context of CBIR. This implies that the space of valid image representations is restricted to those o...