Template Matching with Deformable Diversity Similarity

TL;DR

This paper introduces Deformable Diversity Similarity, a new template matching measure that effectively handles complex deformations, clutter, and occlusions, outperforming current methods in accuracy and efficiency.

Contribution

The paper presents a novel similarity measure for template matching that combines local appearance and geometric information, enhancing robustness to deformations and clutter.

Findings

Outperforms state-of-the-art in detection accuracy

Improves computational complexity

Robust to deformations, clutter, and occlusions

Abstract

We propose a novel measure for template matching named Deformable Diversity Similarity -- based on the diversity of feature matches between a target image window and the template. We rely on both local appearance and geometric information that jointly lead to a powerful approach for matching. Our key contribution is a similarity measure, that is robust to complex deformations, significant background clutter, and occlusions. Empirical evaluation on the most up-to-date benchmark shows that our method outperforms the current state-of-the-art in its detection accuracy while improving computational complexity.