A user-driven model for content-based image retrieval

The intention of image retrieval systems is to provide retrieved results as close to users' expectations as possible. However, users' requirements vary from each other in various application scenarios for the same concept and keywords. In this paper, we introduce a personalized image retrieval model driven by users' operational history. In our simulated system, three types of data, which are browsing time, downloads and grades, are collected to generate a sort criterion for retrieved image sets. According to the criterion, the image collection is classified into a positive group, a negative group and a testing group. Then an SVM classifier is trained with image features extracted from three groups and used to refine retrieved results. We test the proposed method on several image sets. The experimental results show that our model is effective to represent users' demands and help improving retrieval accuracy.