SimPatch: A Nearest Neighbor Similarity Match between Image Patches

TL;DR

This paper introduces SimPatch, a method that uses large image patches and feature extraction to find nearest neighbor patches, improving matching quality for various image analysis tasks.

Contribution

It proposes a novel approach combining large patches with feature-based nearest neighbor algorithms to enhance patch similarity matching.

Findings

Large patches contain more information, improving matching accuracy.

Two different nearest neighbor algorithms are compared and demonstrated.

The method shows promising results in patch similarity tasks.

Abstract

Measuring the similarity between patches in images is a fundamental building block in various tasks. Naturally, the patch-size has a major impact on the matching quality, and on the consequent application performance. We try to use large patches instead of relatively small patches so that each patch contains more information. We use different feature extraction mechanisms to extract the features of each individual image patches which forms a feature matrix and find out the nearest neighbor patches in the image. The nearest patches are calculated using two different nearest neighbor algorithms in this paper for a query patch for a given image and the results have been demonstrated in this paper.