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We present a PatternQuest framework to learn the patterns of interest (i.e., the distribution patterns of positive objects) using classification methods and relevance feedback. To improve the performance of multimedia retrieval, our PatternQuest first employs an efficient feature selection method to extract a low-dimensional feature subspace. With the feature selection, PatternQuest can effectively alleviate the curse of dimensionality for learning-based relevance feedback. To discover patterns of interest in the feature subspace effectively, we propose a multiresolution pattern discovery (MPD) approach, which trains an online pattern classification method known as adaptive random forests to filter negative objects, from the neighborhood of the query to the global scope, in a fine to coarse way. With MPD, our PatternQuest method can iteratively capture the patterns of interest with a little training data from the user's feedback. We have carried out extensive experiments on an image database (with 31,438 Corel images) to demonstrate the effectiveness and robustness of our method.