Exclusive Constrained Discriminative Learning for Weakly-Supervised Semantic Segmentation
文献类型：会议论文
| 作者 | Ying P(应鹏) ; Liu J(刘静); Lu HQ(卢汉清); Ma SD(马颂德)
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| 出版日期 | 2015-10 |
| 会议日期 | 26 – 30,October,2015 |
| 会议地点 | Brisbane, Queensland, Australia |
| 关键词 | Semantic Segmentation Weak Supervision |
| 英文摘要 | ;
How to import image-level labels as weak supervision to direct the region-level labeling task is the core task of weaklysupervised semantic segmentation. In this paper, we focus on designing an effective but simple weakly-supervised constraint, and propose an exclusive constrained discriminative learning model for image semantic segmentation. To be specific, we employ a discriminative linear regression model to assign subsets of superpixels with different labels. During the assignment, we construct an exclusive weakly-supervised constraint term to suppress the labeling responses of each superpixel on the labels outside its parent image-level label set.. Besides, a spectral smoothing term is integrated to encourage that both visually and semantically similar superpixels have similar labels. Combining these terms, we formulate the problem as a convex objective function, which can be easily optimized via alternative iterations. Extensive experiments on MSRC-21 and LabelMe datasets demonstrate the effectiveness of the proposed model. |
| 会议录 | ACM Digital Library
 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/11598]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_图像与视频分析团队 |
| 通讯作者 | Ying P(应鹏) |
| 作者单位 | 中国科学院自动化研究所 |
| 推荐引用方式 GB/T 7714 | Ying P,Liu J,Lu HQ,et al. Exclusive Constrained Discriminative Learning for Weakly-Supervised Semantic Segmentation[C]. 见:. Brisbane, Queensland, Australia. 26 – 30,October,2015. |
入库方式： OAI收割
来源：自动化研究所
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