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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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Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Nonlinear Dimensionality Reduction by Locally Linear Embedding
Roweis, S. T., & Saul, L. K. (2000) · Science · doi:10.1126/science.290.5500.2323 · Cited by 14,965
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
Lazebnik, S., Schmid, C., & Ponce, J. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.68 · Cited by 7,916
Video Google: a text retrieval approach to object matching in videos
Sivic, & Zisserman (2003) · Proceedings Ninth IEEE International Conference on Computer Vision · doi:10.1109/iccv.2003.1238663 · Cited by 6,367
Visual categorization with bags of keypoints
Csurka, G. (2004) · Cited by 4,379
Linear spatial pyramid matching using sparse coding for image classification
Yang, J., Yu, K., Gong, Y., & Huang, T. S. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2009.5206757 · Cited by 2,895
Self-citationEfficient sparse coding algorithms
Lee, H., Battle, A. J., Raina, R., & Ng, A. Y. (2007) · The MIT Press eBooks · doi:10.7551/mitpress/7503.003.0105 · Cited by 2,471
Fei-Fei, L., Fergus, R., & Perona, P. (2007) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2005.09.012 · Cited by 2,359
(2007) · CaltechAUTHORS (California Institute of Technology) · Cited by 2,274
The PASCAL visual object classes challenge 2006 (VOC2006) results
Everingham, M., Zisserman, A., Williams, C. K. I., & Gool, L. V. (2006) · Cited by 2,079
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Research works printed but not linked: 2
- moosmann, 2007, Randomized clustering forests for building fast and discriminative visual vocabularies, Proc of NIPS‘07
- everingham, 0, The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results
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Sun, C., & Nevatia, R. (2013) · Proceedings · doi:10.1109/wacv.2013.6474994 · Cited by 75
Qu, T., Zhang, Q., & Sun, S. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-016-4043-5 · Cited by 74
Breast Cancer Classification From Histopathological Images Using Resolution Adaptive Network
Zhou, Y., Zhang, C., & Gao, S. (2022) · IEEE Access · doi:10.1109/access.2022.3163822 · Cited by 81
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Song, M., Tao, D., Huang, X., Chen, C., & Bu, J. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2183882 · Cited by 75
Deep Neural Network Modeling for Big Data Weather Forecasting
Liu, J. N. K., Hu, Y., He, Y., Chan, P. W., & Lai, L. K. C. (2014) · Studies in big data · doi:10.1007/978-3-319-08254-7_19 · Cited by 75
A Novel Locally Linear KNN Method With Applications to Visual Recognition
Liu, Q., & Liu, C. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2572204 · Cited by 75
Encoding High Dimensional Local Features by Sparse Coding Based Fisher VectorsPreprint
Liu, L., Shen, C., Wang, L., van den Hengel, A., & Wang, C. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1411.6406 · Cited by 71
Weighted group sparse representation for undersampled face recognition
Tang, X., Feng, G., & Cai, J. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.05.012 · Cited by 74
Improving image similarity with vectors of locally aggregated tensors
Picard, D., & Gosselin, P.-H. (2011) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2011.6116641 · Cited by 75
Zhang, Z., Li, F., Chow, T. W. S., Zhang, L., & Yan, S. (2016) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2016.2550016 · Cited by 74
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Zhou, P., Lin, Z., & Zhang, C. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2436951 · Cited by 74
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Luo, X., Xu, Y. D., & Yang, J. J. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.04.027 · Cited by 73
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Zhao, B., Fei-Fei, L., & Xing, E. P. (2011) · Cited by 74
Facial expression recognition in the wild based on multimodal texture features
Sun, B., Li, L., Zhou, G., & He, J. (2016) · Journal of Electronic Imaging · doi:10.1117/1.jei.25.6.061407 · Cited by 74
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Khan, F. S., van de Weijer, J., Anwer, R. M., Felsberg, M., & Gatta, C. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2331759 · Cited by 73
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Fast multi-view segment graph kernel for object classification
Zhang, L., Song, M., Liu, X., Bu, J., & Chen, C. (2012) · Signal Processing · doi:10.1016/j.sigpro.2012.05.012 · Cited by 73
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Liu, B., Wang, Y.-X., Zhang, Y., & Shen, B. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2012.11.018 · Cited by 72
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Zhang, C., Liang, C., Li, L., Liu, J., Huang, Q., & Tian, Q. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2545112 · Cited by 72
Lei, B., Chen, S., Ni, D., & Wang, T. (2016) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2016.00077 · Cited by 72
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Jiang, J., Hu, R., Han, Z., Lü, T., & Huang, K. (2012) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2012.152 · Cited by 71
Sun, B., Li, L., Zuo, T., Chen, Y., Zhou, G., & Wu, X. (2014) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2663204.2666272 · Cited by 71
Jiang, W., Zhang, Z., Li, F., Zhang, L., Zhao, M., & Jin, X. (2015) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2015.2496272 · Cited by 71
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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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