# SuperPatchMatch: an Algorithm for Robust Correspondences using Superpixel Patches

## TL;DR

SuperPatchMatch introduces a superpixel-based patch structure and extends PatchMatch for robust, efficient image segmentation and labeling, outperforming state-of-the-art methods in accuracy and speed.

## Contribution

The paper proposes SuperPatch, a new superpixel-based patch structure, and extends PatchMatch to SuperPatchMatch, enabling robust and fast image segmentation and labeling.

## Key findings

- Outperforms state-of-the-art methods in face labeling
- Achieves higher accuracy in medical image segmentation
- Reduces computational cost significantly

## Abstract

Superpixels have become very popular in many computer vision applications. Nevertheless, they remain underexploited since the superpixel decomposition may produce irregular and non stable segmentation results due to the dependency to the image content. In this paper, we first introduce a novel structure, a superpixel-based patch, called SuperPatch. The proposed structure, based on superpixel neighborhood, leads to a robust descriptor since spatial information is naturally included. The generalization of the PatchMatch method to SuperPatches, named SuperPatchMatch, is introduced. Finally, we propose a framework to perform fast segmentation and labeling from an image database, and demonstrate the potential of our approach since we outperform, in terms of computational cost and accuracy, the results of state-of-the-art methods on both face labeling and medical image segmentation.