TCDesc: Learning Topology Consistent Descriptors

TL;DR

This paper introduces a novel topology-aware triplet loss that incorporates topology measures alongside Euclidean distance to learn more consistent local descriptors, improving performance on benchmark datasets.

Contribution

It proposes a new topology vector and topology distance to enhance triplet loss by considering topological relations among descriptors.

Findings

Outperforms state-of-the-art methods on multiple benchmarks.

Effectively improves descriptor matching accuracy.

Enhances triplet loss with topology information.

Abstract

Triplet loss is widely used for learning local descriptors from image patch. However, triplet loss only minimizes the Euclidean distance between matching descriptors and maximizes that between the non-matching descriptors, which neglects the topology similarity between two descriptor sets. In this paper, we propose topology measure besides Euclidean distance to learn topology consistent descriptors by considering kNN descriptors of positive sample. First we establish a novel topology vector for each descriptor followed by Locally Linear Embedding (LLE) to indicate the topological relation among the descriptor and its kNN descriptors. Then we define topology distance between descriptors as the difference of their topology vectors. Last we employ the dynamic weighting strategy to fuse Euclidean distance and topology distance of matching descriptors and take the fusion result as the positive sample distance in the triplet loss. Experimental results on several benchmarks show that our method performs better than state-of-the-arts results and effectively improves the performance of triplet loss.