Multi-task ranking SVM for image cosegmentation

Abstract

Cosegmentation is the task of simultaneously segmenting multiple images that contain common or similar foreground objects. The assumption that common objects appear in multiple images provides a weak form of supervised prior information, thus cosegmentation usually performs better than unsupervised segmentation methods. Recently, a multi-task learning based cosegmentation method was proposed, and it can simultaneously segment more than two images and easily add different types of prior. However, it has the shortcoming of information loss in the initialization of the multi-task classification model. To ameliorate this problem, in this paper, we propose a novel multi-task ranking SVM model which incorporates multi-task learning and learning to rank into a unified framework. The proposed model is trained using the relative order information between the cosaliency score of pixel pairs. In addition, an optimization algorithm is proposed to optimize the multi-task ranking SVM model based on the alternative direction method of multipliers (ADMM), which ensures that the proposed method is faster than most of state-of-the-art cosegmentation approaches. Finally, the proposed method is evaluated on two widely used benchmark datasets, i.e. CMU iCoseg and MSRC. The experiment results show that the proposed approach is effective and performs better than most of the state-of-the-art works.