We propose a ray-based segmentation method for color images. A segment is represented by a centroid and evenlydistributed rays shooting out from it. First, a bottom-up low-level boundary detection process coarsely constructs candidate segments. Then, two top-down learning processes, mid-level intra-segment learning and high-level inter-segment learning, create the best segments. Segments are created sequentially until all pixels are classified. The number of segments is determined

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... We test our method on the Berkeley Segmentation Dataset. Evaluation results show that our algorithm produces better results than those of the Normalized Cuts segmentation method.