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This paper proposes a new relevance feedback method for image retrieval based on max-min posterior pseudo-probabilities (MMP) framework. We assume that the feature vectors extracted from the relevant images be of the distribution of Gaussian mixture model (GMM). The corresponding posterior pseudoprobability function is used to classify images into two categories: relevant to the user intention and irrelevant. The images relevant to the user intention are returned as the retrieval results which are then labelled as true of false by the user. We further apply MMP training criterion to update the parameter set of the posterior pseudo-probability function from the labelled retrieval results. Subsequently, new retrieval results are returned. Our method of relevance feedback was tested on Corel database and the experimental results show the effectiveness of the proposed method.

RELEVANCE FEEDBACK WITH MAX-MIN POSTERIOR PSEUDO-PROBABILITY FOR IMAGE RETRIEVAL