Feature filtering in relevance feedback of image retrieval based on a statistical approach

Relevance feedback is a powerful tool to grasp the user´s intention in image retrieval systems and has attracted many researchers´ attention since the 1990s. A feature filter, whose parameters are computed by a statistical resampling approach, is proposed in order to select the unique features to characterize the positive samples. A statistical voting procedure is then adopted to rank the candidates after getting rid of irrelevant feature components. Experimental results show that the proposed approach is more efficient and robust than the traditional method.