.png){kind=icon}
返回
A graph-theoretic approach to 3D shape classification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2015.12.130.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Shape classification is an intriguing and challenging problem that lies at the crossroads of computer vision, geometry processing and machine learning. In this paper, we introduce a graph-theoretic approach for 3D shape classification using graph regularized sparse coding in conjunction with the biharmonic distance map. Our unified framework exploits both sparsity and dependence among the features of shape descriptors in a bid to design robust shape signatures that are effective in discriminating between shapes from different classes. In an effort to coherently capture the similarity between feature descriptors, we use multiclass support vector machines for 3D shape classification on mid-level features that are learned via graph regularized sparse coding. Our experiments on two standard 3D shape benchmarks show that the proposed framework not only outperforms the state-of-the-art methods in classification accuracy, but also provides attractive scalability in terms of computational efficiency. (C) 2016 Elsevier B.V. All rights reserved.
Keyword:
Classification
Laplace-Beltrami
Biharmonic distance
Support vector machines
Sparse coding
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
分享收藏Algorithm BOSS (Bag-of-Salient local Spectrums) for non-rigid and partial 3D object retrieval.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏
查看更多内容