• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International conference on graphic and image processing >Weakly Supervised Image Semantic Segmentation Based on Clustering Superpixels
【24h】

Weakly Supervised Image Semantic Segmentation Based on Clustering Superpixels

机译：基于聚类超像素的弱监督图像语义分割

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this paper, we propose an image semantic segmentation model which is trained from image-level labeled images. The proposed model starts with superpixel segmenting, and features of the superpixels are extracted by trained CNN. We introduce a superpixel-based graph followed by applying the graph partition method to group correlated superpixels into clusters. For the acquisition of inter-label correlations between the image-level labels in dataset, we not only utilize label co-occurrence statistics but also exploit visual contextual cues simultaneously. At last, we formulate the task of mapping appropriate image-level labels to the detected clusters as a problem of convex minimization. Experimental results on MSRC-21 dataset and LableMe dataset show that the proposed method has a better performance than most of the weakly supervised methods and is even comparable to fully supervised methods.
机译：在本文中，我们提出了一种图像语义分割模型，该模型是从图像级标记图像中训练出来的。提出的模型从超像素分割开始，并通过训练后的CNN提取超像素的特征。我们介绍了一个基于超像素的图，然后应用图分区方法将相关的超像素分组为群集。为了获取数据集中图像级标签之间的标签间相关性，我们不仅利用了标签共现统计，而且还同时利用了视觉上下文线索。最后，我们提出了将适当的图像级标签映射到检测到的簇的任务，这是凸最小化的问题。在MSRC-21数据集和LableMe数据集上的实验结果表明，与大多数弱监督方法相比，该方法具有更好的性能，甚至可以与完全监督方法相提并论。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号