iSearch: Mining retrieval history for Content-Based Image Retrieval

Relevance feedback is a Powerful technique to bridge the gap between high-level concepts and low-level features, and has been successfully applied to the field of Content-Based Image Retrieval (CBIR) to improve the query accuracy in recent years. In this paper, we propose a novel model (iSearch) which predicts user's information need based on past retrieval history. Based on the prediction, we then transform the feature space based on the user's feedback and employ an Expectation Maximization (EM) approach to simulate the new space by a mixture of Gaussian distributions. The experimental results show that the proposed method is effective and captures the user's information need more precisely.