Simultaneous feature selection and classification for relevance feedback in image retrieval

In image retrieval, relevance feedback uses information, obtained interactively from the user, to understand the user´s perceptions of a query image and to improve retrieval accuracy. We propose simultaneous relevant feature selection and classification using the samples provided by the user to improve retrieval accuracy. The classifier is defined by a separating hyperplane, while the sparse weight vector characterizing the hyperplane defines a small set of relevant features. This set of relevant features is used for classification and can be used for analysis at a later stage. Mutually exclusive sets of images are shown to the user at each iteration to obtain maximum information from the user. Experimental results show that our algorithm performs better than the feature relevance weighting and feature selection schemes and comparably with the classification scheme using SVMs, in terms of retrieval accuracy, and it has the advantage of being faster than the classification scheme using SVMs.