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 development of local image descriptors has been the subject of immense research, and a comprehensive review of related methods is beyond the scope of this work. For a recent survey and evaluation of alternative binary interest point descriptors, we refer the reader to @cite_17 . Here, we only briefly review these and other related representations.
@cite_17
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Performance evaluation of salient features has a long-standing tradition in computer vision. In this paper, we fill the gap of evaluation for the recent wave of binary feature descriptors, which aim to provide robustness while achieving high computational efficiency. We use established metrics to embed our assessment into the body of existing evaluations, allowing us to provide a novel taxonomy unifying both traditional and novel binary features. Moreover, we analyze the performance of different detector and descriptor pairings, which are often used in practice but have been infrequently analyzed. Additionally, we complement existing datasets with novel data testing for illumination change, pure camera rotation, pure scale change, and the variety present in photo-collections. Our performance analysis clearly demonstrates the power of the new class of features. To benefit the community, we also provide a website for the automatic testing of new description methods using our provided metrics and datasets www.cs.unc.edu feature-evaluation.