A PERSONALIZED IMAGE RETRIEVAL BASED ON USER INTEREST MODEL

Abstract

In order to narrow the semantic gap, user interest model plays an important role in personalized image retrieval. A novel personalized image retrieval approach based on user interest model is proposed in this study. User interest model is developed on the basis of short-tem and long-term interests. (1) Short-term interests are represented by collecting visual and semantic features. Visual features are collected by MARS relevance feedback. Semantic features are constructed by building a mapping from image low-level visual features to high-level semantic features on the basis of SVM. (2) Long-term interests are inferred by inference engine from the collected short-term interests. Long-term visual features are collected by the nonlinear gradual forgetting interest inference algorithm and semantic features are obtained by clustering algorithm. After applying to image retrieval, experimental results show that the average recall/precision is significantly improved and a better user satisfaction rate is achieved as well. Furthermore, it demonstrates our model can be efficiently adapted to user interests and matches personalized image retrieval.