Fast interactive image segmentation by discriminative clustering
Dingding Liu, Kari Pulli, Linda G. Shapiro, Yingen Xiong · 2010
We propose a novel and fast interactive image segmentation algorithm for use on mobile phones. Instead of using global optimization, our algorithm begins with an initial over-segmentation using the mean shift algorithm and follows this by discriminative clustering and local neighborhood classification. This procedure obtains better quality results than previous methods that use graph cuts on oversegmented regions or region merging based on maximal similarity, yet its running time is smaller by an order of magnitude. We compare and analyze the strengths and limitations of the three approaches and have implemented our algorithm as part of an interactive object cut out application running on a mobile phone.