Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Segmentation by Data-Driven Markov Chain Monte Carlo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Guiding Model Search Using Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Comparing clusterings: an axiomatic view
ICML '05 Proceedings of the 22nd international conference on Machine learning
Unsupervised segmentation of natural images via lossy data compression
Computer Vision and Image Understanding
Natural image segmentation with adaptive texture and boundary encoding
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Segmentation of Multivariate Mixed Data via Lossy Data Coding and Compression
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hybrid image segmentation using watersheds and fast region merging
IEEE Transactions on Image Processing
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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.