# Robust Shape Regularity Criteria for Superpixel Evaluation

## TL;DR

This paper introduces a new shape regularity metric for superpixels that considers convexity, repartition, and smoothness, providing a more robust and relevant evaluation than traditional circularity measures.

## Contribution

The authors propose a novel superpixel shape regularity metric that improves evaluation robustness and relevance by addressing limitations of existing circularity-based measures.

## Key findings

- The new metric is robust to scale and noise.
- It enables more relevant comparison of superpixel methods.
- Traditional circularity measures are inadequate for superpixel evaluation.

## Abstract

Regular decompositions are necessary for most superpixel-based object recognition or tracking applications. So far in the literature, the regularity or compactness of a superpixel shape is mainly measured by its circularity. In this work, we first demonstrate that such measure is not adapted for superpixel evaluation, since it does not directly express regularity but circular appearance. Then, we propose a new metric that considers several shape regularity aspects: convexity, balanced repartition, and contour smoothness. Finally, we demonstrate that our measure is robust to scale and noise and enables to more relevantly compare superpixel methods.