| Large-Scale Weakly Supervised Object Localization via Latent Category Learning | |
Wang, Chong1; Huang, Kaiqi1 ; Ren, Weiqiang1; Zhang, Junge1; Maybank, Steve2
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| 发表期刊 | IEEE TRANSACTIONS ON IMAGE PROCESSING
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| 2015-04-01 | |
| 卷号 | 24期号:4页码:1371-1385 |
| 文章类型 | Article |
| 摘要 | Localizing objects in cluttered backgrounds is challenging under large-scale weakly supervised conditions. Due to the cluttered image condition, objects usually have large ambiguity with backgrounds. Besides, there is also a lack of effective algorithm for large-scale weakly supervised localization in cluttered backgrounds. However, backgrounds contain useful latent information, e.g., the sky in the aeroplane class. If this latent information can be learned, object-background ambiguity can be largely reduced and background can be suppressed effectively. In this paper, we propose the latent category learning (LCL) in large-scale cluttered conditions. LCL is an unsupervised learning method which requires only image-level class labels. First, we use the latent semantic analysis with semantic object representation to learn the latent categories, which represent objects, object parts or backgrounds. Second, to determine which category contains the target object, we propose a category selection strategy by evaluating each category's discrimination. Finally, we propose the online LCL for use in large-scale conditions. Evaluation on the challenging PASCAL Visual Object Class (VOC) 2007 and the large-scale imagenet large-scale visual recognition challenge 2013 detection data sets shows that the method can improve the annotation precision by 10% over previous methods. More importantly, we achieve the detection precision which outperforms previous results by a large margin and can be competitive to the supervised deformable part model 5.0 baseline on both data sets. |
| 关键词 | Weakly Supervised Learning Object Localization Latent Semantic Analysis Large-scale |
| WOS标题词 | Science & Technology ; Technology |
| 关键词[WOS] | IMAGE CLASSIFICATION ; NETWORKS |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS研究方向 | Computer Science ; Engineering |
| WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| WOS记录号 | WOS:000351088600004 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.ia.ac.cn/handle/173211/8086 |
| 专题 | 智能感知与计算研究中心 |
| 作者单位 | 1.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China 2.Univ London, Birkbeck Coll, Dept Comp Sci & Informat Syst, London WC1E 7HU, England |
| 第一作者单位 | 模式识别国家重点实验室 |
| 推荐引用方式 GB/T 7714 | Wang, Chong,Huang, Kaiqi,Ren, Weiqiang,et al. Large-Scale Weakly Supervised Object Localization via Latent Category Learning[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2015,24(4):1371-1385. |
| APA | Wang, Chong,Huang, Kaiqi,Ren, Weiqiang,Zhang, Junge,&Maybank, Steve.(2015).Large-Scale Weakly Supervised Object Localization via Latent Category Learning.IEEE TRANSACTIONS ON IMAGE PROCESSING,24(4),1371-1385. |
| MLA | Wang, Chong,et al."Large-Scale Weakly Supervised Object Localization via Latent Category Learning".IEEE TRANSACTIONS ON IMAGE PROCESSING 24.4(2015):1371-1385. |
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| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Chong Wang_Large-Sca（5133KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 | |
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