Automatic foreground extraction via joint CRF and online learning

A novel approach is proposed for automatic foreground extraction which aims to segment out all foreground objects from the background in the image. The segmentation problem is formulated as an iterative energy minimisation of the conditional random field (CRF), which can be efficiently optimised by graph-cuts. The energy minimisation is initialised and modulated by a soft location map predicted by a discriminative classifier which is learned on-the-fly from a set of segmented exemplar images. Iteratively minimising the CRF energy leads to optimal segmentation. Experimental results on the Pascal visual object classes (VOC) 2010 segmentation dataset, a widely acknowledged difficult dataset, show that the proposed approach outperforms the state-of-the-art techniques.