LATCH: Learned Arrangements of Three Patch Codes
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Abstract (arXiv)
We present a novel means of describing local image appearances using binary strings. Binary descriptors have drawn increasing interest in recent years due to their speed and low memory footprint. A known shortcoming of these representations is their inferior performance compared to larger, histogram based descriptors such as the SIFT. Our goal is to close this performance gap while maintaining the benefits attributed to binary representations. To this end we propose the Learned Arrangements of Three Patch Codes descriptors, or LATCH. Our key observation is that existing binary descriptors are at an increased risk from noise and local appearance variations. This, as they compare the values of pixel pairs; changes to either of the pixels can easily lead to changes in descriptor values, hence damaging its performance. In order to provide more robustness, we instead propose a novel means of comparing pixel patches. This ostensibly small change, requires a substantial redesign of the descriptors themselves and how they are produced. Our resulting LATCH representation is rigorously compared to state-of-the-art binary descriptors and shown to provide far better performance for similar computation and space requirements.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Jan 2015.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 1; 2024: 1.
6 papers.
- 2021Revisiting Binary Local Image Description for Resource Limited Devicescited by 3
- 2024BEBLID: Boosted efficient binary local image descriptorcited by 3
- 2021Deep Metric Learning for Ground Imagescited by 1
- 2022Needle In A Haystack, Fast: Benchmarking Image Perceptual Similarity Metrics At Scalecited by 1
- 2023Illumination-insensitive Binary Descriptor for Visual Measurement Based on Local Inter-patch Invariancecited by 1
- 2021Model-Based Parameter Optimization for Ground Texture Based Localization Methodscited by 0
- 2024BEBLID: Boosted efficient binary local image descriptor7 Feb 2024
- 2023Illumination-insensitive Binary Descriptor for Visual Measurement Based on Local Inter-patch Invariance13 May 2023
- 2022Needle In A Haystack, Fast: Benchmarking Image Perceptual Similarity Metrics At Scale1 Jun 2022
- 2021Deep Metric Learning for Ground Images3 Sep 2021
- 2021Model-Based Parameter Optimization for Ground Texture Based Localization Methods3 Sep 2021
- 2021Revisiting Binary Local Image Description for Resource Limited Devices18 Aug 2021