Learning hidden semantic cues using support vector clustering

This paper presents a method to infer hidden semantic cues by accumulating the knowledge learned from relevance feedback sessions. We propose to explicitly represent a semantic space using a probabilistic model. In short-term learning, we apply the general 2-class SVM classification to initialize the semantic space. Once the accumulated semantic space becomes impractically large, we propose using support vector clustering (SVC) to construct a more compact and still meaningful semantic space with lower dimensionality. Given a dimension-reduced semantic space, we then perform the image query in terms of the semantic attributes instead of merely the visual features. Our experimental results and comparisons demonstrate that the proposed semantic representation as well as the SVC-based technique indeed achieves promising results.