Joint shape segmentation with linear programming
- 12 December 2011
- journal article
- research article
- Published by Association for Computing Machinery (ACM) in ACM Transactions on Graphics
- Vol. 30 (6), 1-12
- https://doi.org/10.1145/2070781.2024159
Abstract
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.Keywords
Funding Information
- National Science Foundation (8.09E+12)
This publication has 19 references indexed in Scilit:
- Unsupervised co-segmentation of a set of shapes via descriptor-space spectral clusteringACM Transactions on Graphics, 2011
- Style-content separation by anisotropic part scalesACM Transactions on Graphics, 2010
- Contextual Part Analogies in 3D ObjectsInternational Journal of Computer Vision, 2009
- Consistent segmentation of 3D modelsComputers & Graphics, 2009
- A survey on Mesh Segmentation TechniquesComputer Graphics Forum, 2008
- TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture, Layout, and ContextInternational Journal of Computer Vision, 2007
- MAP Estimation Via Agreement on Trees: Message-Passing and Linear ProgrammingIEEE Transactions on Information Theory, 2005
- Hierarchical mesh decomposition using fuzzy clustering and cutsPublished by Association for Computing Machinery (ACM) ,2003
- Normalized cuts and image segmentationIeee Transactions On Pattern Analysis and Machine Intelligence, 2000
- Objective Criteria for the Evaluation of Clustering MethodsJournal of the American Statistical Association, 1971