Scalable image co-segmentation using color and covariance features

This paper focuses on producing fast and accurate co-segmentation to a pair of images that is scalable and able to apply multimodal features. We present a general solution for this purpose and specifically propose a noniterative and fully unsupervised method using pointwise color and regional covariance features for image co-segmentation. The scalability and generality of our method mainly attribute to the superpixel-level irregular graph formulation and multi-feature joint clustering. Through a unified similarity metric, the contributions of multiple features are finally embodied into the co-segmentation energy function. Experiments on common dataset validate the superior scalability of our method over state-of-the-art alternatives and its capability of generating comparable or even better labeling accuracy at the same time. We also find that multifeature co-segmentation usually produces better labeling accuracy than using single color feature only.