Abstract
Most segmentation methods are based on a relatively simple score, designed to lend itself to relatively efficient optimization. We take the opposite approach and suggest more complex segmentation scores that are based on a mixture of on-line and off-line learning processes and rely on rich descriptors. The score is evaluated by a segmentation process which uses exploration-exploitation to search for good segments in various scales and shapes. We test our algorithm in a foreground-background segmentation task, given a minimal prior which is just a single seed point inside the object of interest. Results on two image databases are presented and compared with earlier approaches.
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Peles, D., Lindenbaum, M. (2012). A Segmentation Quality Measure Based on Rich Descriptors and Classification Methods. In: Bruckstein, A.M., ter Haar Romeny, B.M., Bronstein, A.M., Bronstein, M.M. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2011. Lecture Notes in Computer Science, vol 6667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24785-9_34
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DOI: https://doi.org/10.1007/978-3-642-24785-9_34
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