(ML)-L-4: Maximum margin Multi-instance Multi-cluster Learning for scene modeling
文献类型：期刊论文
| 作者 | Zhang, Tianzhu1,2 ; Liu, Si1,2; Xu, Changsheng1,2; Lu, Hanqing1,2
|
| 刊名 | PATTERN RECOGNITION
 |
| 出版日期 | 2013-10-01 |
| 卷号 | 46期号:10页码:2711-2723 |
| 关键词 | Scene understanding Maximum margin clustering Multiple instance learning (MIL) Gaussian Mixture Model (GMM) Constrained Concave-Convex Procedure (CCCP) |
| 英文摘要 | Automatically learning and grouping key motion patterns in a traffic scene captured by a static camera is a fundamental and challenging task for intelligent video surveillance. To learn motion patterns, trajectory obtained by object tracking is parameterized, and scene image is spatially and evenly divided into multiple regular cell blocks which potentially contain several primary motion patterns. Then, for each block, Gaussian Mixture Model (GMM) is adopted to learn its motion patterns based on the parameters of trajectories. Grouping motion pattern can be done by clustering blocks indirectly, and each cluster of blocks corresponds to a certain motion pattern. For one particular block, each of its motion pattern (Gaussian component) can be viewed as an instance, and all motion patterns (Gaussian components) constitute a bag which can correspond to multiple semantic clusters. Therefore, blocks can be grouped as a Multi-instance Multi-cluster Learning (MIMCL) problem, and a novel Maximum Margin Multi-instance Multi-cluster Learning ((ML)-L-4) algorithm is proposed. To avoid processing a difficult optimization problem, (ML)-L-4 is further relaxed and solved by making use of a combination of the Cutting Plane method and Constrained Concave-Convex Procedure (CCCP). Extensive experiments are conducted on multiple real world video sequences containing various patterns and the results validate the effectiveness of our proposed approach. (C) 2013 Elsevier Ltd. All rights reserved. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| 研究领域[WOS] | Computer Science ; Engineering |
| 关键词[WOS] | CLASSIFICATION ; CATEGORIZATION ; SEGMENTATION ; PATTERNS ; SYSTEM ; VIDEO |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000320477400009 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/2877]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队 |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.China Singapore Inst Digital Media, Singapore 119615, Singapore |
| 推荐引用方式 GB/T 7714 | Zhang, Tianzhu,Liu, Si,Xu, Changsheng,et al. (ML)-L-4: Maximum margin Multi-instance Multi-cluster Learning for scene modeling[J]. PATTERN RECOGNITION,2013,46(10):2711-2723. |
| APA | Zhang, Tianzhu,Liu, Si,Xu, Changsheng,&Lu, Hanqing.(2013).(ML)-L-4: Maximum margin Multi-instance Multi-cluster Learning for scene modeling.PATTERN RECOGNITION,46(10),2711-2723. |
| MLA | Zhang, Tianzhu,et al."(ML)-L-4: Maximum margin Multi-instance Multi-cluster Learning for scene modeling".PATTERN RECOGNITION 46.10(2013):2711-2723. |
入库方式： OAI收割
来源：自动化研究所
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