• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Arabian Journal for Science and Engineering >Unsupervised Shape Co-segmentation Based on Transformation Network
【24h】

Unsupervised Shape Co-segmentation Based on Transformation Network

机译：基于变换网络的无监督形状协同细分

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Unsupervised co-segmentation is one type of shape segmentation. It segments a set of 3D shapes into meaningful parts and creates a correspondence between parts simultaneously without any labeled data. Clustering-based co-segmentation is based on the correlation analysis in a descriptor space and has received increasing attention. In this paper, we propose a co-segmentation method, in which a transformation network for data representation is trained by extreme learning machine, embedding shape primitives into more discriminant feature spaces, so as to achieve better segmentation performance. Thus, co-segmentation can be implemented by clustering on lower dimensions based on the transformation network, so the execution is more efficient. Moreover, once the transformation network is trained, it can be applied to the data representation acquisition process without re-computing similarity parameters. In order to create and train the transformation network, the correlation of shape primitives is utilized. Therefore, an affinity matrix construction method based on parameter-free and high-efficiency simplex sparse representation is introduced. This construction of correlation avoids the blindness of parameter setting. Experimental results show that the proposed co-segmentation method is effective and efficient. In addition, it also can deal with incremental co-segmentation when the dataset is expanded.
机译：无监督共分割是形状分割的一种类型。它将一组3D形状分割成有意义的部分，并同时在各部分之间创建对应关系，而无需任何标记数据。基于聚类的共分割基于描述符空间中的相关性分析，并且受到越来越多的关注。在本文中，我们提出了一种共分割方法，其中使用极限学习机训练用于数据表示的转换网络，将形状图元嵌入到更具区分性的特征空间中，以实现更好的分割性能。因此，可以通过基于转换网络在较低维度上进行聚类来实现共细分，因此执行效率更高。而且，一旦训练了转换网络，就可以将其应用于数据表示获取过程，而无需重新计算相似性参数。为了创建和训练转换网络，利用了形状基元的相关性。因此，介绍了一种基于无参数和高效单纯形稀疏表示的亲和矩阵构造方法。这种相关性的构造避免了参数设置的盲目性。实验结果表明，该方法是有效的。此外，扩展数据集时，它还可以处理增量共分割。

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