Appearance Estimation and Image Segmentation via Tensor Factorization

Image Segmentation is one of the core tasks in Computer Vision, and solving it often depends on modeling the image appearance data via the color distributions of each of its constituent regions. Whereas many segmentation algorithms handle the appearance model dependence using alternation or implicit methods, we propose here a new approach to directly estimate them from the image without prior information on the underlying segmentation. Our method uses local high-order color statistics from the image as an input to a tensor factorization-based estimator for latent variable models. This approach is able to estimate models in multi-region images and automatically output the regions' proportions without prior user interaction, overcoming the drawbacks of a prior attempt to this problem. We also demonstrate the performance of our proposed method in many challenging synthetic and real imaging scenarios and show that it leads to an efficient segmentation algorithm.