Probabilistic relevance feedback with binary semantic feature vectors

Abstract

For information retrieval, relevance feedback is an important technique. This paper proposes a relevance feedback technique which is based on a probabilistic framework. The binary feature vectors in our experiment are high-level semantic features of trademark logo images, each feature representing the presence or absence of a certain shape or object. The images were labeled by human experts of the trademark office. We compared our probabilistic method with several existing methods such as MARS, MindReader, and one-class SVM. Our method outperformed the others.