The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Learning Discriminative Appearance-Based Models Using Partial Least Squares
Abstract
Appearance information is essential for applications such as tracking and people recognition. One of the main problems of using appearance-based discriminative models is the ambiguities among classes when the number of persons being considered increases. To reduce the amount of ambiguity, we propose the use of a rich set of feature descriptors based on color, textures and edges. Another issue regarding appearance modeling is the limited number of training samples available for each appearance. The discriminative models are created using a powerful statistical tool called partial least squares (PLS), responsible for weighting the features according to their discriminative power for each different appearance. The experimental results, based on appearance-based person recognition, demonstrate that the use of an enriched feature set analyzed by PLS reduces the ambiguity among different appearances and provides higher recognition rates when compared to other machine learning techniques.
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Cited by
DeepReID: Deep Filter Pairing Neural Network for Person Re-identification
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Person re-identification by symmetry-driven accumulation of local features
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Person Re-identification by Multi-Channel Parts-Based CNN with Improved Triplet Loss Function
Cheng, D., Gong, Y., Zhou, S., Wang, J., & Zheng, N. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.149 · Cited by 1,247
MARS: A Video Benchmark for Large-Scale Person Re-Identification
Zheng, L., Bie, Z., Sun, Y., Wang, J., Su, C., Wang, S., & Tian, Q. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46466-4_52 · Cited by 965
Person Re-identification by Descriptive and Discriminative Classification
Hirzer, M., Beleznai, C., Roth, P. M., & Bischof, H. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-21227-7_9 · Cited by 961
Metadata
- Authors
- Schwartz, W. R., & Davis, L. S.
- Venue
- 2009 XXII Brazilian Symposium on Computer Graphics and Image Processing
- Publisher
- IEEE
- Published
- 2009-10
- Pages
- 322-329
- OpenAlex
- W2122378993
- Cited by
- 351
- References
- 23
- FWCI
- 14.72
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Schwartz, W. R., & Davis, L. S. (2009). Learning Discriminative Appearance-Based Models Using Partial Least Squares. 2009 XXII Brazilian Symposium on Computer Graphics and Image Processing. https://doi.org/10.1109/sibgrapi.2009.42
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