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Infinite Hidden Markov Random Fields have been proposed for image segmentation as a solution to the problem of automatically determining the number of regions in an image; however, the model does not maintain identity of segmented regions among multiple images. In order to identify segmented regions in images, we developed Hierarchical Dirichlet Process Markov Random Fields. Our model maintains global identification of segmented regions in multiple images by incorporating the idea of hierarchical modeling and automatically determines the number of segmented regions in each image. We show an experimental comparison between the previous model and our proposed model by changing the observation features from RGB value to color histogram features.