LDAHash: Improved Matching with Smaller Descriptors
Open accessAbstract
SIFT-like local feature descriptors are ubiquitously employed in computer vision applications such as content-based retrieval, video analysis, copy detection, object recognition, photo tourism, and 3D reconstruction. Feature descriptors can be designed to be invariant to certain classes of photometric and geometric transformations, in particular, affine and intensity scale transformations. However, real transformations that an image can undergo can only be approximately modeled in this way, and thus most descriptors are only approximately invariant in practice. Second, descriptors are usually high dimensional (e.g., SIFT is represented as a 128-dimensional vector). In large-scale retrieval and matching problems, this can pose challenges in storing and retrieving descriptor data. We map the descriptor vectors into the Hamming space in which the Hamming metric is used to compare the resulting representations. This way, we reduce the size of the descriptors by representing them as short binary strings and learn descriptor invariance from examples. We show extensive experimental validation, demonstrating the advantage of the proposed approach.
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The optimization approach transforms hash function learning into a regression problem with binary code B as the regression target, avoiding a relaxation strategy for nonconvex binary constraints.
“More importantly, our optimization approach transforms the hash function learning into a regression problem with binary code B as the regression target, thus avoiding the use of a relaxation strategy to solve the nonconvex equation with binary constraints in most published works [3], [8], [18], [35], [48], [57], [58].”
From Unsupervised Deep Video Hashing via Balanced Code for Large-Scale Video Retrieval (2019) · cites this paper as [8]
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- Authors
- Strecha, C., Bronstein, A. M., Bronstein, M. M., & Fua, P.
- Affiliations
- École Polytechnique Fédérale de Lausanne (CH)
- Technion – Israel Institute of Technology (IL)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2012-01
- Volume
- 34
- Issue
- 1
- Pages
- 66-78
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2134514757
- Cited by
- 605
- References
- 39
- Citations by year
- 2026: 2 · 2025: 9 · 2024: 9 · 2023: 10 · 2022: 13 · 2021: 32 · 2020: 42 · 2019: 43 · 2018: 43 · 2017: 100 · 2016: 63 · 2015: 83 · 2014: 64 · 2013: 53 · 2012: 28
- FWCI
- 53.50
- Type
- journal article
- Language
- en
- Open access
- green
- infoscience.epfl.ch
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- PubMed (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Funders
- École Polytechnique Fédérale de Lausanne
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- local descriptors, descriptor reduction, Hamming space, binary codes, image matching, content-based image retrieval, large-scale retrieval, affine invariance, scale invariance
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