Comprehensive Saliency Fusion for Object Co-segmentation

Object co-segmentation has drawn significant attention in recent years, thanks to its clarity on the expected foreground, the shared object in a group of images. Saliency fusion has been one of the promising ways to carry it out. However, prior works either fuse saliency maps of the same image or saliency maps of different images to extract the expected foregrounds. Also, they rely on hand-crafted saliency extraction and correspondence processes in most cases. This paper revisits the problem and proposes fusing saliency maps of both the same image and different images. It also leverages advances in deep learning for the saliency extraction and correspondence processes. Hence, we call it comprehensive saliency fusion. Our experiments reveal that our approach achieves much-improved object co-segmentation results compared to prior works on important benchmark datasets such as iCoseg, MSRC, and Internet Images.

I. Introduction

Object co-segmentation is the task of extracting common objects as foregrounds in a group of images. Compared to the single image segmentation, where it’s difficult to ascertain what’s foreground, we have a clear-cut definition of what foreground we wish to extract in the object co-segmentation task. Rother et.al. [1] first introduced the concept of co-segmentation by developing a histogram matching method to extract common parts from a pair of images. However, Vicente et.al. [2] were the first to propose that co-segmentation should be about things (or objects) and there is a need to incorporate a measure of objectness in the models. They proposed a Random Forest classifier to find the similarity between a pair of proposed segmentation of objects in each image of a group followed by the A* search algorithm [3] to find the segmented objects with maximum similarity score. The major applications of object co-segmentation include image grouping [4], object recognition [5], and object tracking [6] which are among the fundamental tasks of Computer Vision.