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Retrieval of overlapping and touching objects using hidden Markov models

A content-based image retrieval system for overlapping and touching objects based on hidden Markov models is introduced. In a first step, unsupervised clustering in color and position space is performed in order to separate the objects. The clusters are handed over to the feature extraction, which is basically a polar subsampling, and finally rotation invariant Markov models are trained on those features. After presenting a query object, the HMMs which represent the individual clusters in the images are matched against the feature sequence calculated on the query image. Those database elements whose corresponding Markov models generated the highest similarity scores are retrieved. Three different clustering techniques, namely k-means clustering, LBG-algorithm and EM-algorithm are evaluated. Retrieval efficiencies up to 56.25% have been achieved on this challenging task.