Superpixel-Based Image Segmentation Using Squared 2-Wasserstein Distances

2026-01-22

AI Summary

This work addresses the challenge of insufficient image segmentation accuracy under complex conditions such as severe illumination non-uniformity by proposing a two-stage clustering method. In the first stage, superpixels are generated via linear least-squares assignment; in the second stage, these superpixels are greedily merged into semantic regions based on the squared 2-Wasserstein distance between their empirical distributions. The key innovation lies in the novel introduction of discrete optimal transport into the superpixel merging phase, replacing conventional mean-color metrics with the squared 2-Wasserstein distance to achieve mathematical consistency across both clustering levels. Experimental results demonstrate that the proposed approach significantly improves segmentation accuracy on challenging images while maintaining high computational efficiency.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Clustering
• Search and Optimization: Distributed Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

We present an efficient method for image segmentation in the presence of strong inhomogeneities. The approach can be interpreted as a two-level clustering procedure: pixels are first grouped into superpixels via a linear least-squares assignment problem, which can be viewed as a special case of a discrete optimal transport (OT) problem, and these superpixels are subsequently greedily merged into object-level segments using the squared 2-Wasserstein distance between their empirical distributions. In contrast to conventional superpixel merging strategies based on mean-color distances, our framework employs a distributional OT distance, yielding a mathematically unified formulation across both clustering levels. Numerical experiments demonstrate that this perspective leads to improved segmentation accuracy on challenging images while retaining high computational efficiency.

Problem

• image segmentation
• inhomogeneities
• superpixel
• Wasserstein distance
• optimal transport

Innovation

• superpixel
• optimal transport
• Wasserstein distance
• image segmentation
• distributional clustering