Saliency-based initialization of Gaussian mixture models for fully-automatic object segmentation

Abstract

The conventional object segmentation methods often degrade their performance due to the requirement of user interaction and/or the incomplete colour appearance models. In this Letter, the authors propose a novel design method of a colour appearance model for accurate and fully-automatic object segmentation by using saliency maps. The authors initialise the Gaussian mixture models (GMMs) to describe the colour appearance of the foreground objects and the background, respectively, where the mean vectors, covariance matrices, and mixing coefficients are updated adaptively such that more salient pixels have larger weights to update the GMM for the foreground objects while less salient pixels have larger weights to update the GMM for the background, respectively. Experiments are performed on MSRC, iCoseg, and PASCAL datasets and we show that the proposed method outperforms the existing methods quantitatively and qualitatively.