Sparse representation for human gait recognition
Sparsity-based algorithms recently have received great interests from statistics, signal processing, machine learning as well as computer vision. In this master thesis, it discusses the sparse representation based algorithms for computer vision problem, including the independent sparse representatio...
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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
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2011
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| Online Access: | http://hdl.handle.net/10356/46243 |
| _version_ | 1811686302735663104 |
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| author | Zeng, Zinan |
| author2 | Xu Dong |
| author_facet | Xu Dong Zeng, Zinan |
| author_sort | Zeng, Zinan |
| collection | NTU |
| description | Sparsity-based algorithms recently have received great interests from statistics, signal processing, machine learning as well as computer vision. In this master thesis, it discusses the sparse representation based algorithms for computer vision problem, including the independent sparse representation (ISR), locality-constraint coding, group sparse representation (GSR). Based on these existing algorithms, two new algorithms referred to as locality-constrain group sparse representation (LGSR) and multiple-kernel group sparse representation (MKGSR) are proposed. Comprehensive experiments for Human Gait Recognition (HGR) using USF HumanID Gait database show that the two newly proposed methods, LGSR and MKGSR respectively achieve the best Rank-1 and Rank-5 recognition accuracy. |
| first_indexed | 2024-10-01T04:58:16Z |
| format | Thesis |
| id | ntu-10356/46243 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:58:16Z |
| publishDate | 2011 |
| record_format | dspace |
| spelling | ntu-10356/462432023-03-04T00:34:12Z Sparse representation for human gait recognition Zeng, Zinan Xu Dong School of Computer Engineering Centre for Multimedia and Network Technology DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Sparsity-based algorithms recently have received great interests from statistics, signal processing, machine learning as well as computer vision. In this master thesis, it discusses the sparse representation based algorithms for computer vision problem, including the independent sparse representation (ISR), locality-constraint coding, group sparse representation (GSR). Based on these existing algorithms, two new algorithms referred to as locality-constrain group sparse representation (LGSR) and multiple-kernel group sparse representation (MKGSR) are proposed. Comprehensive experiments for Human Gait Recognition (HGR) using USF HumanID Gait database show that the two newly proposed methods, LGSR and MKGSR respectively achieve the best Rank-1 and Rank-5 recognition accuracy. Master of Engineering 2011-07-08T04:13:57Z 2011-07-08T04:13:57Z 2011 2011 Thesis http://hdl.handle.net/10356/46243 en 63 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Zeng, Zinan Sparse representation for human gait recognition |
| title | Sparse representation for human gait recognition |
| title_full | Sparse representation for human gait recognition |
| title_fullStr | Sparse representation for human gait recognition |
| title_full_unstemmed | Sparse representation for human gait recognition |
| title_short | Sparse representation for human gait recognition |
| title_sort | sparse representation for human gait recognition |
| topic | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision |
| url | http://hdl.handle.net/10356/46243 |
| work_keys_str_mv | AT zengzinan sparserepresentationforhumangaitrecognition |