Two Birds With One Stone: A Unified Approach to Saliency and Co-Saliency Detection via Multi-Instance Learning

Abstract

Saliency detection on an individual image as well as co-saliency detection from a group of images are currently popular topics or reflect future trends more recently due to their importance and challenging roles in computer vision. In many cases, co-saliency detection is usually dependent on the single-image saliency detection results. Nevertheless, most efforts have been made to tackle them separately and not much attention has been paid to tackling them together in a unified idea. Being aware of these two tasks are highly related, the difference from previous surveys is that this paper applies a unified framework by employing a multi-instance learning (MIL) algorithm to resolve both the issues, and formulating single-image saliency and co-saliency detection as top-down weakly supervised learning paradigm. Specifically, for single-image saliency detection, we utilize the evidence confidence-support vector machine algorithm to learn a discriminant model to predict the saliency on test images. For co-saliency detection from image group, we concatenate the EC values and saliency scores to obtain the final results of co-saliency detection. By observing the importance of the selection of negative bags in the MIL framework, we also introduce a novel selection strategy of negative bags to improve the robustness of the proposed method. Experimental results on publicly available image benchmark data sets have demonstrated that the proposed unified framework can achieve competitive performances as compared with the state-of-the-art algorithms in terms of accuracy and effectiveness.