Towards Hypothesis Testing and Lossy Minimum Description Length: A Unified Segmentation Framework
We propose a novel algorithm for unsupervised segmentation of images based on statistical hypothesis testing. We model the distribution of the image texture features as a mixture of Gaussian distributions so that multi-normal population hypothesis test is used as a similarity measure between region features. Our algorithm iteratively merges adjacent regions that are “most similar”, until all pairs of adjacent regions are sufficiently “dissimilar”. Standing on a higher level, we give a hypothesis testing segmentation framework (HT), which allows different definitions of merging criterion and termination condition. Further more, we derive an interesting connection between HT framework and previous lossy minimum description length (LMDL) segmentation. We prove that under specific merging criterion and termination condition, LMDL can be unified as a special case under HT framework. This theoretical result also gives novel insights and improvements on LMDL based algorithms. We conduct experiments on the Berkeley Segmentation Dataset, and our algorithm achieves superior results compared to other popular methods including LMDL based algorithms.
KeywordsImage Segmentation Natural Image Good Segmentation Optimal Segmentation Segmentation Framework
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