Superpixel-Based Object Class Segmentation Using Conditional Random Fields

Abstract

Object class segmentation (OCS) is a key issue in semantic scene labeling and understanding. Its general principle consists of naming object entities into scenes according to their intrinsic visual features as well as their dependencies. In this paper, we propose a novel superpixel-based framework for object class segmentation using conditional random fields (CRFs). The framework proceeds in two steps: (i) superpixel label estimate; and (ii) CRF label propagation. Step (i) is achieved using multi-scale boosted classifiers over superpixels and makes it possible to find coarse estimates of initial labels. Fine labeling is afterward achieved in Step (ii), using an anisotropic contrast sensitive pairwise function designed in order to characterize the intrinsic interaction potentials between objects according to 4-neighborhoods. Finally, a higher-order criterion is applied to enforce region label consistency of OCS. Experimental results demonstrate the effectiveness of the proposed framework.