Image Patch Similarity Through a Meta-Learning Metric Based Approach

Abstract

This paper proposes a novel approach to learn the best representation of the image patches to determine the similarity degree between cross-spectral regions (patches). The present work tackles this problem using a few-shot metric based meta-learning framework able to compare image regions and determining a similarity measure to decide if there is similarity between the compared patches. Our model is training end-to-end from scratch. Experimental results have shown that the proposed approach effectively estimates the similarity of the patches and, comparing it with the state of the art approaches, shows better results.