Improving the Initial Image Retrieval Set by Inter-Query Learning with One-Class SVMs

Relevance Feedback attempts to reduce the semantic gap between a user’s perception of similarity and a feature-based representation of an image by asking the user to provide feedback regarding the relevance or non-relevance of the retrieved images. This is intra-query learning. However, in most current systems, all prior experience is lost whenever a user generates a new query thus inter-query information is not used. In this paper, we focus on the possibility of incorporating prior experience (obtained from the historical interaction of users with the system) to improve the retrieval performance on future queries. We propose learning one-class SVMs from retrieval experience to represent the set memberships of users’ query concepts. Using a fuzzy classification approach, this historical knowledge is then incorporated into future queries to improve the retrieval performance. In order to learn the set membership of a user’s query concept, a one-class SVM maps the relevant or training images into a nonlinearly transformed kernel-induced feature space and attempts to include most of those images into a hyper-sphere. The use of kernels allows the one-class SVM to deal with the non-linearity of the distribution of training images in an efficient manner, while at the same time, providing good generalization. The proposed approach is evaluated against real data sets and the results obtained confirm the effectiveness of using prior experience in improving retrieval performance.