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3D Shape Segmentation and Labeling via Extreme Learning Machine
Author(s) -
Xie Zhige,
Xu Kai,
Liu Ligang,
Xiong Yueshan
Publication year - 2014
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.12434
Subject(s) - segmentation , artificial intelligence , computer science , extreme learning machine , face (sociological concept) , pattern recognition (psychology) , scale space segmentation , segmentation based object categorization , cut , minimum spanning tree based segmentation , computer vision , image segmentation , classifier (uml) , artificial neural network , social science , sociology
We propose a fast method for 3D shape segmentation and labeling via Extreme Learning Machine (ELM). Given a set of example shapes with labeled segmentation, we train an ELM classifier and use it to produce initial segmentation for test shapes. Based on the initial segmentation, we compute the final smooth segmentation through a graph‐cut optimization constrained by the super‐face boundaries obtained by over‐segmentation and the active contours computed from ELM segmentation. Experimental results show that our method achieves comparable results against the state‐of‐the‐arts, but reduces the training time by approximately two orders of magnitude, both for face‐level and super‐face‐level, making it scale well for large datasets. Based on such notable improvement, we demonstrate the application of our method for fast online sequential learning for 3D shape segmentation at face level, as well as realtime sequential learning at super‐face level.