TY - GEN
T1 - Joint shape segmentation with linear programming
AU - Huang, Qixing
AU - Koltun, Vladlen
AU - Guibas, Leonidas
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: We are grateful to Mirela Ben-Chen, Siddhartha Chaudhuri, and Evangelos Kalogerakis for their comments on this paper. This work was supported in part by NSF grants 0808515 and 1011228, a Stanford-KAUST AEA grant, and a Stanford Graduate Fellowship.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.
PY - 2011
Y1 - 2011
N2 - We present an approach to segmenting shapes in a heterogenous shape database. Our approach segments the shapes jointly, utilizing features from multiple shapes to improve the segmentation of each. The approach is entirely unsupervised and is based on an integer quadratic programming formulation of the joint segmentation problem. The program optimizes over possible segmentations of individual shapes as well as over possible correspondences between segments from multiple shapes. The integer quadratic program is solved via a linear programming relaxation, using a block coordinate descent procedure that makes the optimization feasible for large databases. We evaluate the presented approach on the Princeton segmentation benchmark and show that joint shape segmentation significantly outperforms single-shape segmentation techniques. © 2011 ACM.
AB - We present an approach to segmenting shapes in a heterogenous shape database. Our approach segments the shapes jointly, utilizing features from multiple shapes to improve the segmentation of each. The approach is entirely unsupervised and is based on an integer quadratic programming formulation of the joint segmentation problem. The program optimizes over possible segmentations of individual shapes as well as over possible correspondences between segments from multiple shapes. The integer quadratic program is solved via a linear programming relaxation, using a block coordinate descent procedure that makes the optimization feasible for large databases. We evaluate the presented approach on the Princeton segmentation benchmark and show that joint shape segmentation significantly outperforms single-shape segmentation techniques. © 2011 ACM.
UR - http://hdl.handle.net/10754/598683
UR - http://dl.acm.org/citation.cfm?doid=2024156.2024159
UR - http://www.scopus.com/inward/record.url?scp=82455171688&partnerID=8YFLogxK
U2 - 10.1145/2024156.2024159
DO - 10.1145/2024156.2024159
M3 - Conference contribution
SN - 9781450308076
BT - Proceedings of the 2011 SIGGRAPH Asia Conference on - SA '11
PB - Association for Computing Machinery (ACM)
ER -