Dictionary learning based superpixels clustering for weakly-supervised semantic segmentation
文献类型：会议论文
| 作者 | Peng Ying ; Jing Liu; Hanqing Lu
|
| 出版日期 | 2015 |
| 会议日期 | September 27-30, 2016 |
| 会议地点 | Quebec City, QC, Canada |
| 关键词 | Dictionary Learning Weak Supervision Semantic Segmentation |
| 英文摘要 | ; The task of weakly-supervised semantic segmentation is solved by assigning image-level labels to over-segmented superpixels. Considering that superpixels are geometrically and semantically ambiguous for label assignment, we propose a joint solution of semantic segmentation to enhance the learnability of superpixels. First, our model includes a spectral clustering item and a discriminative clustering item to obtain some clustering subsets of superpixels (ideally semantic regions), which are more separable semantically than independent superpixels. Second, sparse coding based feature for superpixel is adopted to make the representation robust to noise, and the dictionary for the sparse representation is learned together with the above clustering items. Third, a weakly supervised item for superpixels, transferred from image-level labels, is attached. We jointly formulate the above problems as a non-convex objective function, and optimize it by the constraint concave-convex programming (CCCP) algorithm. Extensive experiments on MSRC-21 and LabelMe datasets prove the effectiveness of our approach. |
| 会议录 | 无
 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/11600]  |
| 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 | Jing Liu |
| 推荐引用方式 GB/T 7714 | Peng Ying,Jing Liu,Hanqing Lu. Dictionary learning based superpixels clustering for weakly-supervised semantic segmentation[C]. 见:. Quebec City, QC, Canada. September 27-30, 2016. |
入库方式： OAI收割
来源：自动化研究所
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