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Locality-constrained Linear Coding for image classification
Abstract
The traditional SPM approach based on bag-of-features (BoF) requires nonlinear classifiers to achieve good image classification performance. This paper presents a simple but effective coding scheme called Locality-constrained Linear Coding (LLC) in place of the VQ coding in traditional SPM. LLC utilizes the locality constraints to project each descriptor into its local-coordinate system, and the projected coordinates are integrated by max pooling to generate the final representation. With linear classifier, the proposed approach performs remarkably better than the traditional nonlinear SPM, achieving state-of-the-art performance on several benchmarks. Compared with the sparse coding strategy [22], the objective function used by LLC has an analytical solution. In addition, the paper proposes a fast approximated LLC method by first performing a K-nearest-neighbor search and then solving a constrained least square fitting problem, bearing computational complexity of O(M + K2). Hence even with very large codebooks, our system can still process multiple frames per second. This efficiency significantly adds to the practical values of LLC for real applications.
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Metadata
- Authors
- Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., & Gong, Y.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- NEC (United States) (US)
- Venue
- 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2010-06
- Pages
- 3360-3367
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2027922120
- Semantic Scholar
- 2f7713dcc35e7c05becf3be5522f36c9546b0364
- Cited by
- 3,100
- References
- 28
- Citations by year
- 2026: 10 · 2025: 24 · 2024: 42 · 2023: 51 · 2022: 66 · 2021: 100 · 2020: 124 · 2019: 198 · 2018: 275 · 2017: 336 · 2016: 420 · 2015: 473 · 2014: 417 · 2013: 296 · 2012: 176
- FWCI
- 154.25
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- 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 › Advanced Neural Network Applications
- Keywords
- locality-constrained linear coding, bag of visual words, image classification, local coordinate system, max pooling, linear classifiers, k-nearest neighbor search, constrained least squares, real-time processing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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