@inproceedings{fu2014object-based,
author = {Fu, Huazhu and Xu, Dong and Zhang, Bao and Lin, Steve},
title = {Object-based Multiple Foreground Video Co-segmentation},
booktitle = {Computer Vision and Pattern Recognition (CVPR)},
year = {2014},
month = {June},
abstract = {We present a video co-segmentation method that uses category-independent object proposals as its basic element and can extract multiple foreground objects in a video set. The use of object elements overcomes limitations of low-level feature representations in separating complex foregrounds and backgrounds. We formulate object-based co-segmentation as a co-selection graph in which regions with foreground-like characteristics are favored while also accounting for intra-video and inter-video foreground coherence. To handle multiple foreground objects, we expand the co-selection graph model into a proposed multi-state selection graph model (MSG) that optimizes the segmentations of different objects jointly. This extension into the MSG can be applied not only to our co-selection graph, but also can be used to turn any standard graph model into a multi-state selection solution that can be optimized directly by the existing energy minimization techniques. Our experiments show that our object-based multiple foreground video co-segmentation method (ObMiC) compares well to related techniques on both single and multiple foreground cases.},
url = {http://approjects.co.za/?big=en-us/research/publication/object-based-multiple-foreground-video-co-segmentation-2/},
edition = {Computer Vision and Pattern Recognition (CVPR)},
}