Coarse-to-Fine Segmentation With Shape-Tailored Scale Spaces

TL;DR

This paper introduces a novel segmentation method that emphasizes coarse structures over fine details by integrating multi-scale data terms derived from the heat equation, improving robustness and performance in motion segmentation tasks.

Contribution

The authors propose a scale-space based energy formulation that favors coarse segmentation, with an efficient optimization approach that avoids full scale space computation, applicable to motion segmentation.

Findings

Less sensitive to clutter and fine-scale noise

Achieves better motion segmentation performance

Computationally efficient due to simplified optimization

Abstract

We formulate a general energy and method for segmentation that is designed to have preference for segmenting the coarse structure over the fine structure of the data, without smoothing across boundaries of regions. The energy is formulated by considering data terms at a continuum of scales from the scale space computed from the Heat Equation within regions, and integrating these terms over all time. We show that the energy may be approximately optimized without solving for the entire scale space, but rather solving time-independent linear equations at the native scale of the image, making the method computationally feasible. We provide a multi-region scheme, and apply our method to motion segmentation. Experiments on a benchmark dataset shows that our method is less sensitive to clutter or other undesirable fine-scale structure, and leads to better performance in motion segmentation.