Retrieval of difficult image classes using svd-based relevance feedback

Abstract

User-defined classes in large generalist image databases are often composed of several groups of images and span very different scales in the space of low-level visual descriptors. The interactive retrieval of such image classes is then very difficult. To addess his challenge, we propose and evaluate here two general mprovements of SVM-based relevance feedback methods. First, to optimize the transfer of information between the user and the system, we focus on the criterion employed by the system for selecting the images presented to the user at every feedback round. We put forward a new active learning selection criterion that minimizes redundancy between the candidate images shown to the user. Second, for image classes having very different scales, we find that a high sensitivity of the SVM to the scale of the data brings about a low retrieval performance. We then argue that insensitivity to scale is desirable in this context and we show how to obtain it by the use of specific kernel functions. The experimental evaluation of both ranking and classification performance on several image databases confirms the effectiveness of our selection criterion and of the use of kernels that reduce the sensitivity of SVMs to the scale of the data