A segmentation quality measure based on rich descriptors and classification methods

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. © 2012 Springer-Verlag.