Construction of user preference profile in a personalized image retrieval

In order to reduce the semantic gap between low-level visual features and high-level semantics, a novel approach for constructing user preference profile in personalized image retrieval is proposed. In proposed approach, the user interest is divided into two parts: the short-term interest and the long-term interest. Semantic feature vector in the short-term interest is constructed by building the correlation between image low-level visual features and high-level semantics on the basis of SVM after collecting the visual feature vector in the short-term interest with relevance feedback. Moreover, the visual feature vector in the long-term interest can be collected by the non-linear gradual forgetting interest inference algorithm. Semantic feature vector in the long-term is constructed with clustering algorithm. Experiments results show that the average recall/precision are significantly improved and satisfied by personalized user as well.