Classification of images based on Hidden Markov Models

Abstract : We propose to use hidden Markov models (HMMs) to classify images. Images are modeled by extracting symbols corresponding to 3x3 binary neighborhoods of interest points, and by ordering these symbols by decreasing saliency order, thus obtaining strings of symbols. HMMs are learned from sets of strings modeling classes of images. The method has been tested on the SIMPLIcity database and shows an improvement over competing approaches based on interest points. We also evaluate these approaches for classifying thumbnail images, i.e., low resolution images.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01437635
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Submitted on : Tuesday, January 17, 2017 - 1:53:39 PM
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Marc Mouret, Christine Solnon, Christian Wolf. Classification of images based on Hidden Markov Models. IEEE Workshop on Content Based Multimedia Indexing, Jun 2009, Chania, Crète, Greece. pp.169-174, ⟨10.1109/CBMI.2009.22⟩. ⟨hal-01437635⟩

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