Combining local regularity estimation and total variation optimization for scale-free texture segmentation

TL;DR

This paper introduces a novel method for segmenting scale-free textures by combining local regularity estimation with total variation optimization, effectively balancing bias and variance in the process.

Contribution

It proposes new variational formulations using total variation and wavelet leaders for improved scale-free texture segmentation.

Findings

Enhanced segmentation accuracy on synthetic textures

Effective handling of bias-variance trade-off in regularity estimation

Successful application to real-world texture images

Abstract

Texture segmentation constitutes a standard image processing task, crucial to many applications. The present contribution focuses on the particular subset of scale-free textures and its originality resides in the combination of three key ingredients: First, texture characterization relies on the concept of local regularity ; Second, estimation of local regularity is based on new multiscale quantities referred to as wavelet leaders ; Third, segmentation from local regularity faces a fundamental bias variance trade-off: In nature, local regularity estimation shows high variability that impairs the detection of changes, while a posteriori smoothing of regularity estimates precludes from locating correctly changes. Instead, the present contribution proposes several variational problem formulations based on total variation and proximal resolutions that effectively circumvent this trade-off. Estimation and segmentation performance for the proposed procedures are quantified and compared on synthetic as well as on real-world textures.