Abstract: Given a collection of images which contains objects from the same category, the co-segmentation methods aim at simultaneously segmenting such common objects in each image. Most of existing co-segmentation approaches rely on comput-ing similarities inter-regions representing foregrounds in these images. However, region similarity measurement is challenging due to the large appearance variations among objects in the same category. In addition, for real-world images which have cluttered backgrounds, the existing co-segmentation approaches miss sufficient robustness to extract the common object from the background. In this paper, we propose a new co-segmentation method which takes advantage of the reliable segmentation of few selected images, in order to guide the segmentation of the remaining images in the collection. A random sample of images is first selected from the image collection. Then, the selected images are segmented using an interactive segmentation method. These segmentation results are used to construct positive/negative samples of the targeted common object and background regions respectively. Finally, these samples are propagated to the remain-ing images in the collection through computing both local and global consistency. The experiments on the iCoseg and MSRC datasets demonstrate the performance and robustness of the proposed method.

Rachida Es-Salhi, Imane Daoudi and Hamid El Ouardi, “Image Co-Segmentation via Examples Guidance” International Journal of Advanced Computer Science and Applications(IJACSA), 10(1), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100165

@article{Es-Salhi2019,
title = {Image Co-Segmentation via Examples Guidance},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100165},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100165},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {1},
author = {Rachida Es-Salhi and Imane Daoudi and Hamid El Ouardi}
}