A comprehensive review and new taxonomy on superpixel segmentation

TL;DR

This paper provides a comprehensive review and introduces a new taxonomy for superpixel segmentation methods, evaluating 20 strategies and establishing a benchmark to advance understanding and development in this field.

Contribution

It offers a new taxonomy categorizing superpixel methods by processing steps and feature levels, and presents a benchmark for their assessment.

Findings

Identifies key trends and trade-offs among superpixel methods.

Evaluates methods based on nine criteria including robustness and visual quality.

Provides a publicly available benchmark for future research.

Abstract

Superpixel segmentation consists of partitioning images into regions composed of similar and connected pixels. Its methods have been widely used in many computer vision applications since it allows for reducing the workload, removing redundant information, and preserving regions with meaningful features. Due to the rapid progress in this area, the literature fails to catch up on more recent works among the compared ones and to categorize the methods according to all existing strategies. This work fills this gap by presenting a comprehensive review with new taxonomy for superpixel segmentation, in which methods are classified according to their processing steps and processing levels of image features. We revisit the recent and popular literature according to our taxonomy and evaluate 20 strategies based on nine criteria: connectivity, compactness, delineation, control over the number of superpixels, color homogeneity, robustness, running time, stability, and visual quality. Our experiments show the trends of each approach in pixel clustering and discuss individual trade-offs. Finally, we provide a new benchmark for superpixel assessment, available at https://github.com/IMScience-PPGINF-PucMinas/superpixel-benchmark.