Automated co-superpixel generation via graph matching

In this paper, a novel ‘co-superpixel’ generation method is proposed via the graph matching. The co-superpixel can capture the common semantic information in coupled images. Therefore, it is significant for various applications in visual pattern recognition. Specifically, we first introduce a superpixel correspondence method based on the graph matching. The main property is that it has the ability to capture the consistent intermediate-level semantic information in coupled images, which can represent the region-based similarity rather than the conventional similarity based on low-level vision features. Second, a new co-superpixel generation method is proposed by the superpixel-merging incorporated with the graph matching cost and the adjacent superpixel appearance similarity in coupled images simultaneously. Furthermore, we extend the proposed co-superpixel method to tackle the object matching problem. The experimental results show that the object matching can be effectively addressed by the co-superpixel. The proposed method is effective for challenging cases in which object appearance changes, deformation and background clutter.