An Automatic Framework for Figure-Ground Segmentation in Cluttered Backgrounds

This paper proposes an automatic framework for figure-ground segmentation of edged images in the presence of cluttered background. Our work employs perceptual grouping concepts to characterize image segments by means of their saliency, which is computed via tensor voting. The main innovation of our work is a case-based thresholding scheme which iteratively eliminates edge segments with low-saliency in multiple scales, preserving those that are more likely to belong to foreground. The key idea is classifying saliency histograms in several cases by considering the relative position of the modes of the figure/ground distributions and applying specific actions in each case. We have performed extensive experiments in order to evaluate our framework both quantitatively and qualitatively, including real images from the Berkeley dataset.