CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Exclusive Constrained Discriminative Learning for Weakly-Supervised Semantic Segmentation
Ying P(应鹏); Liu J(刘静); Lu HQ(卢汉清); Ma SD(马颂德)
2015-10
Conference NameACM Multimedia Conference 2015
Source PublicationACM Digital Library
Conference Date26 – 30,October,2015
Conference PlaceBrisbane, Queensland, Australia
Abstract
;
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.
KeywordSemantic Segmentation Weak Supervision
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/11598
Collection模式识别国家重点实验室_图像与视频分析
Corresponding AuthorYing P(应鹏)
Affiliation中国科学院自动化研究所
Recommended Citation
GB/T 7714
Ying P,Liu J,Lu HQ,et al. Exclusive Constrained Discriminative Learning for Weakly-Supervised Semantic Segmentation[C],2015.
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