Improving Image Retrieval Performance with Negative Relevance Feedback

Learning user perception of an image is a challenging issue in interactive content-based image retrieval (CBIR) systems. These systems employ relevance feedback mechanism to learn user perception in terms of a set of model-parameters and in turn iteratively improve the retrieval performance. Since the quantity of user feedback is expected to be small, learning the user’s perception essentially involves parameter estimation with very few training points. We propose a novel, and more efficient method for relevance feedback in this paper. Contrary to existing geometric model-based relevance feedback methods, the proposed technique explicitly uses information about irrelevant data points to estimate the parameters of the model. This algorithm iteratively updates the parameters of the similarity metric so as to fit the relevant examples while excluding the irrelevant ones. This is achieved by modifying the weights associated with the relevant examples. Experiments on image and synthetic datasets demonstrate the retrieval effectiveness of the proposed approach.