Unsupervised Segmentation of Color Images

Abstract : A novel technique for unsupervised learning in feature space is presented. The feature space is considered as composed of two distinct sources, “mode” and “valley”, in the point of view of information theory. An entropy-based thresholding is taken to distinguish the discretized cells in the feature space. The cells labeled as “mode” are then chained to form mode areas. Thereafter a modified Akaike's information criterion is proposed to solve the cluster validity problem. After all the parameters are estimated, a labeling algorithm is developed based on the majority game theory. The method is applied to color image segmentation. The segmentation process is completely autonomous
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Communication dans un congrès
ICIP 1998 - International Conference on Image Processing, Oct 1998, Chicago, United States. IEEE, 1998, 〈10.1109/ICIP.1998.727203〉
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https://hal.inria.fr/hal-01087883
Contributeur : Shan Yu <>
Soumis le : jeudi 27 novembre 2014 - 04:31:01
Dernière modification le : samedi 27 janvier 2018 - 01:30:51

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Guodong Guo, Shan Yu, Songde Ma. Unsupervised Segmentation of Color Images. ICIP 1998 - International Conference on Image Processing, Oct 1998, Chicago, United States. IEEE, 1998, 〈10.1109/ICIP.1998.727203〉. 〈hal-01087883〉

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