Implicit kernel presentation aware object segmentation framework

Abstract

Given a set of training shapes and an input image with a shape similar to some of the elements in the training set, this paper introduces a new implicit kernel sparse model with a twofold goal. First, to obtain an implicit kernel sparse neighbor based combination that best represents the object. Second, to accurately segment the object taking into accounts both the high-level implicit kernel presentation and the low-level image information. A new energy function that combines the variational image segmentation with the implicit kernel presentation is introduced to accomplish both goals simultaneously. The experimental results on the public datasets show the superior capabilities of the proposed model.