梳理超像素分割新分类，帮你看清各类方法优劣。
A comprehensive review and new taxonomy on superpixel segmentation
- 按处理步骤和特征层级重新分类超像素算法
- 评估20种策略，覆盖9个关键指标
- 提供新基准测试，适合研究者对比选型
超像素分割旨在将图像划分为由相似且相连像素组成的区域。该方法因能降低计算负担、去除冗余信息并保留有意义的区域特征，被广泛应用于计算机视觉任务中。由于该领域进展迅速，现有文献难以涵盖最新工作，且缺乏对所有方法策略的系统分类。本文填补这一空白，提出一种新的超像素分割综合评述与分类体系，依据处理步骤和图像特征处理层级对方法进行归类。我们基于新分类体系回顾了近期主流文献，并根据九项标准（连通性、紧凑性、轮廓精度、超像素数量控制、颜色同质性、鲁棒性、运行时间、稳定性、视觉质量）评估20种策略。实验揭示各类方法在像素聚类中的趋势及各自权衡。最后，我们发布了新基准测试平台，地址为 https://github.com/IMScience-PPGINF-PucMinas/superpixel-benchmark。
原文摘要 · Abstract (English)
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.
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