LATCH: Learned Arrangements of Three Patch Codes (1501.03719v1)

Abstract: 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.

• The paper introduces LATCH, a novel binary descriptor enhancing robustness by comparing learned arrangements of pixel patches instead of individual pixel pairs.
• LATCH uses a data-driven approach to select discriminative patch triplets, significantly outperforming state-of-the-art binary descriptors and competing with histogram-based ones like SIFT.
• This efficient descriptor has significant implications for real-time computer vision applications needing fast local image matching, such as image retrieval and structure from motion.

An Overview of "LATCH: Learned Arrangements of Three Patch Codes"

The paper "LATCH: Learned Arrangements of Three Patch Codes" by Gil Levi and Tal Hassner introduces a novel approach to enhancing the effectiveness of binary descriptors for local image appearances. Binary descriptors are favored for their computational efficiency and low memory usage; however, they traditionally underperform in comparison with histogram-based descriptors such as SIFT due to their sensitivity to noise and local variations. The authors propose the LATCH descriptor, a binary descriptor that aims to bridge this performance gap while retaining the advantages of binary representations.

LATCH innovatively shifts from comparing individual pixel pairs to evaluating patch triplets. Existing binary descriptors are susceptible to noise because they rely on pairwise pixel comparisons. Any alteration in the pixel values can lead to significant changes in descriptor values, impacting its robustness. LATCH mitigates this issue by utilizing comparisons of pixel patches, each producing a bit in the binary string, thereby increasing descriptor stability against noise and local appearance alterations.

The authors employ a data-driven approach for selecting the most discriminative patch triplets. Using the labeled dataset from Brown et al. (2011), containing pairs of image patches labeled as similar or not-similar, they evaluate combinations of patches based on their ability to differentiate between these labels. Triplets are selected if their responses are not highly correlated with already chosen triplets, enhancing LATCH’s discriminative power.

In comprehensive tests against several benchmarks, including the Oxford dataset and the Learning Local Image Descriptors dataset, LATCH consistently outperforms state-of-the-art binary descriptors in discriminative tasks. It even shows competitive results with more computationally-intensive histogram descriptors like SIFT and SURF under certain conditions, despite maintaining fast computation and memory efficiency. LATCH achieves this remarkable balance with a small increase in computational cost, verified through empirical runtime analysis.

The implications of LATCH are significant in computer vision applications dependent on local image matching, including image retrieval, classification, and structure from motion (SfM). In practical terms, LATCH provides an efficient tool for real-time systems and large-scale data analysis where rapid computation is crucial. Theoretically, it sets a precedent for combining data-driven learning with binary descriptor design, potentially inspiring further exploration in automatic learning methods for feature description.

Looking ahead, advancements in AI might explore extending LATCH’s framework to more complex learning paradigms, exploiting deep learning methodologies for further refinement of patch selection and arrangement. The approach of leveraging supervised learning for feature extraction represents a promising avenue in bridging the performance gap between binary and histogram descriptors, paving the way for adaptive, intelligent descriptor designs in the future.

This paper contributes significantly to filling the existing gap in binary descriptor performance, demonstrating that through strategic design and learning, binary descriptors can achieve both efficiency and effectiveness akin to their histogram-based counterparts, offering a robust tool for a wide range of computer vision applications.