Cascade Learning by Optimally Partitioning
文献类型：期刊论文
| 作者 | Pang, Yanwei1; Cao, Jiale1; Li, Xuelong2
|
| 刊名 | ieee transactions on cybernetics
 |
| 出版日期 | 2016-09-12 |
| 期号 | 99 |
| 关键词 | Adaptive boosting Classification (of information) Computation theory Costs Face recognition Iterative methods Object detection Object recognition Optimal systems |
| ISSN号 | 21682267 |
| 产权排序 | 2 |
| 英文摘要 | cascaded adaboost classifier is a well;known efficient object detection algorithm. the cascade structure has many parameters to be determined. most of existing cascade learning algorithms are designed by assigning detection rate and false positive rate to each stage either dynamically or statically. their objective functions are not directly related to minimum computation cost. these algorithms are not guaranteed to have optimal solution in the sense of minimizing computation cost. on the assumption that a strong classifier is given, in this paper, we propose an optimal cascade learning algorithm (icascade) which iteratively partitions the strong classifiers into two parts until predefined number of stages are generated. icascade searches the optimal partition point r of each stage by directly minimizing the computation cost of the cascade. theorems are provided to guarantee the existence of the unique optimal solution. theorems are also given for the proposed efficient algorithm of searching optimal parameters r. once a new stage is added, the parameter r for each stage decreases gradually as iteration proceeds, which we call decreasing phenomenon. moreover, with the goal of minimizing computation cost, we develop an effective algorithm for setting the optimal threshold of each stage. in addition, we prove in theory why more new weak classifiers in the current stage are required compared to that of the previous stage. experimental results on face detection and pedestrian detection demonstrate the effectiveness and efficiency of the proposed algorithm. © 2013 ieee. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/28361]  |
| 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.School of Electronic Information Engineering, Tianjin University, Tianjin; 300072, China 2.Center for Optical Imagery Analysis and Learning, State Key Laboratory of Transient Optics and Photonics, Xi'An Institute of Optics and Precision Mechanics, Xian; 710119, China |
| 推荐引用方式 GB/T 7714 | Pang, Yanwei,Cao, Jiale,Li, Xuelong. Cascade Learning by Optimally Partitioning[J]. ieee transactions on cybernetics,2016(99). |
| APA | Pang, Yanwei,Cao, Jiale,&Li, Xuelong.(2016).Cascade Learning by Optimally Partitioning.ieee transactions on cybernetics(99). |
| MLA | Pang, Yanwei,et al."Cascade Learning by Optimally Partitioning".ieee transactions on cybernetics .99(2016). |
入库方式： OAI收割
来源：西安光学精密机械研究所
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