Estimating Appearance Models for Image Segmentation via Tensor Factorization

TL;DR

This paper introduces a novel tensor factorization method to directly estimate appearance models for image segmentation, enabling automatic, multi-region segmentation without prior information and demonstrating improved performance on synthetic and real images.

Contribution

The paper presents a new tensor factorization-based approach to estimate appearance models directly from images, eliminating the need for prior segmentation information.

Findings

Effective in multi-region images

Automatically estimates region proportions

Outperforms previous methods in challenging scenarios

Abstract

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 it its constituent regions. Whereas many segmentation algorithms handle the appearance models 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 tensor factorization-based estimator for latent variable models. This approach is able to estimate models in multiregion images and automatically output the regions proportions without prior user interaction, overcoming the drawbacks from 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.