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Learning Spatio-Temporal Representations for Action Recognition: A Genetic Programming Approach
文献类型：期刊论文
| 作者 | Liu, Li1,2; Shao, Ling1,2; Li, Xuelong3 ; Lu, Ke4,5
|
| 刊名 | ieee transactions on cybernetics
 |
| 出版日期 | 2016 |
| 卷号 | 46期号:1页码:158-170 |
| ISSN号 | 2168-2267 |
| 关键词 | Action recognition feature extraction feature learning genetic programming (GP) spatio-temporal descriptors |
| 通讯作者 | shao, l |
| 产权排序 | 3 |
| 英文摘要 | extracting discriminative and robust features from video sequences is the first and most critical step in human action recognition. in this paper, instead of using handcrafted features, we automatically learn spatio-temporal motion features for action recognition. this is achieved via an evolutionary method, i.e., genetic programming (gp), which evolves the motion feature descriptor on a population of primitive 3d operators (e.g., 3d-gabor and wavelet). in this way, the scale and shift invariant features can be effectively extracted from both color and optical flow sequences. we intend to learn data adaptive descriptors for different datasets with multiple layers, which makes fully use of the knowledge to mimic the physical structure of the human visual cortex for action recognition and simultaneously reduce the gp searching space to effectively accelerate the convergence of optimal solutions. in our evolutionary architecture, the average cross-validation classification error, which is calculated by an support-vector-machine classifier on the training set, is adopted as the evaluation criterion for the gp fitness function. after the entire evolution procedure finishes, the best-so-far solution selected by gp is regarded as the (near-) optimal action descriptor obtained. the gp-evolving feature extraction method is evaluated on four popular action datasets, namely kth, hmdb51, ucf youtube, and hollywood2. experimental results show that our method significantly outperforms other types of features, either hand-designed or machine-learned. |
| 学科主题 | computer science, artificial intelligence ; computer science, cybernetics |
| WOS标题词 | science & technology ; technology |
| 类目[WOS] | computer science, artificial intelligence ; computer science, cybernetics |
| 研究领域[WOS] | computer science |
| 关键词[WOS] | particle swarm optimization ; feature-selection ; classification ; features ; interpolation ; algorithm ; framework |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000367144300015 |
| 公开日期 | 2016-02-25 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/27736]  |
| 专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
| 作者单位 | 1.Nanjing Univ Informat Sci & Technol, Coll Elect & Informat Engn, Nanjing 210044, Jiangsu, Peoples R China 2.Northumbria Univ, Dept Comp Sci & Digital Technol, Newcastle Upon Tyne NE1 8ST, Tyne & Wear, England 3.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr Opt Imagery Anal & Learning, Xian 710119, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 5.Beijing Ctr Math & Informat Interdisciplinary Sci, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Li,Shao, Ling,Li, Xuelong,et al. Learning Spatio-Temporal Representations for Action Recognition: A Genetic Programming Approach[J]. ieee transactions on cybernetics,2016,46(1):158-170. |
| APA | Liu, Li,Shao, Ling,Li, Xuelong,&Lu, Ke.(2016).Learning Spatio-Temporal Representations for Action Recognition: A Genetic Programming Approach.ieee transactions on cybernetics,46(1),158-170. |
| MLA | Liu, Li,et al."Learning Spatio-Temporal Representations for Action Recognition: A Genetic Programming Approach".ieee transactions on cybernetics 46.1(2016):158-170. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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