Extracting foreground masks towards object recognition

Effective segmentation prior to recognition has been shown to improve recognition performance. However, most segmentation algorithms adopt methods which are not explicitly linked to the goal of object recognition. Here we solve a related but slightly different problem in order to assist object recognition more directly - the extraction of a foreground mask, which identifies the locations of objects in the image. We propose a novel foreground/background segmentation algorithm that attempts to segment the interesting objects from the rest of the image, while maximizing an objective function which is tightly related to object recognition. We do this in a manner which requires no class-specific knowledge of object categories, using a probabilistic formulation which is derived from manually segmented images. The model includes a geometric prior and an appearance prior, whose parameters are learnt on the fly from images that are similar to the query image. We use graph-cut based energy minimization to enforce spatial coherence on the model's output. The method is tested on the challenging VOC09 and VOC10 segmentation datasets, achieving excellent results in providing a foreground mask. We also provide comparisons to the recent segmentation method of [7].