Retrieval of overlapping and touching objects using hidden markov models

S. Müller, F. Wallhoff, G. Rigoll

Research output: Contribution to conferencePaperpeer-review


In this paper 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 the 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 effiencies up to 56.25% have been achieved on this challenging task.

Original languageEnglish
Number of pages4
StatePublished - 2001
Externally publishedYes
EventIEEE International Conference on Image Processing (ICIP) - Thessaloniki, Greece
Duration: 7 Oct 200110 Oct 2001


ConferenceIEEE International Conference on Image Processing (ICIP)


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