# Feature Fusion for Robust Patch Matching With Compact Binary Descriptors

## TL;DR

This paper introduces a convolutional network framework that fuses pixel domain and transformed domain features to learn compact, discriminative binary patch descriptors, significantly improving matching accuracy across multiple datasets.

## Contribution

It proposes a novel feature fusion approach combining pixel and transformed domain features within a deep learning framework for robust patch matching.

## Key findings

- Outperforms state-of-the-art methods in accuracy
- Achieves better rate and complexity trade-offs
- Effective across multiple datasets

## Abstract

This work addresses the problem of learning compact yet discriminative patch descriptors within a deep learning framework. We observe that features extracted by convolutional layers in the pixel domain are largely complementary to features extracted in a transformed domain. We propose a convolutional network framework for learning binary patch descriptors where pixel domain features are fused with features extracted from the transformed domain. In our framework, while convolutional and transformed features are distinctly extracted, they are fused and provided to a single classifier which thus jointly operates on convolutional and transformed features. We experiment at matching patches from three different datasets, showing that our feature fusion approach outperforms multiple state-of-the-art approaches in terms of accuracy, rate, and complexity.