Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/17006
Title: Multi-view ensemble manifold regularization for 3D object recognition
Authors: Hong, C
Yu, J
You, J 
Chen, X
Tao, D
Keywords: 3D object recognition
Hypergraph
Manifold learning
Multi-view fusion
Support vector machine
Issue Date: 2015
Publisher: Elsevier
Source: Information sciences, 2015, v. 320, p. 395-405 How to cite?
Journal: Information sciences 
Abstract: View-based methods are popular in 3D object recognition. However, current methods with traditional classifiers are usually based on one-to-one view matching and fail to capture the structure information of multiple views. Some multi-view based methods take different views into consideration, but they still treat views separately. In this paper, we propose a novel 3D object recognizing method based on multi-view data fusion, called Multi-view Ensemble Manifold Regularization (MEMR). In this method, we model image features with a regularization term for SVM. To train this modified SVM, multi-view learning is achieved with alternating optimization. Hypergraph construction is used to better capture the connectivity among views. Experimental results show that the accuracy rate has been improved by 20-25%, which demonstrates the effectiveness of the proposed method.
URI: http://hdl.handle.net/10397/17006
ISSN: 0020-0255
EISSN: 1872-6291
DOI: 10.1016/j.ins.2015.03.032
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