Transductive 3D Shape Segmentation using Sparse Reconstruction

We propose a transductive shape segmentation algorithm, which can transfer prior segmentation results in database to new shapes without explicitly specification of prior category information. Our method first partitions an input shape into a set of segmentations as a data preparation, and then a lin...

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Vydáno v:Computer graphics forum Ročník 33; číslo 5; s. 107 - 115
Hlavní autoři: Xu, Weiwei, Shi, Zhouxu, Xu, Mingliang, Zhou, Kun, Wang, Jingdong, Zhou, Bin, Wang, Jinrong, Yuan, Zhenming
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Blackwell Publishing Ltd 01.08.2014
Témata:
Algorithms
Analysis
Categories
Categories and Subject Descriptors (according to ACM CCS)
Computer graphics
I.3.3 [Computer Graphics]: Shape Analysis-Shape SegmentationSparsity
Image processing systems
Integer programming
Reconstruction
Segmentation
Segments
Similarity
Studies
Three dimensional
ISSN:0167-7055, 1467-8659
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Shrnutí:We propose a transductive shape segmentation algorithm, which can transfer prior segmentation results in database to new shapes without explicitly specification of prior category information. Our method first partitions an input shape into a set of segmentations as a data preparation, and then a linear integer programming algorithm is used to select segments from them to form the final optimal segmentation. The key idea is to maximize the segment similarity between the segments in the input shape and the segments in database, where the segment similarity is computed through sparse reconstruction error. The segment‐level similarity enables to handle a large amount of shapes with significant topology or shape variations with a small set of segmented example shapes. Experimental results show that our algorithm can generate high quality segmentation and semantic labeling results in the Princeton segmentation benchmark.
Bibliografie:ark:/67375/WNG-Z3HKJ09S-R
ArticleID:CGF12436
istex:07A8EFF8371F8F5CC706B69C5817B6617B3578B6
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.12436