Multi-view ensemble manifold regularization for 3D object recognition

Chaoqun Hong, Jun Yu, Jia You, Xuhui Chen, Dapeng Tao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

77 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)395-405
Number of pages11
JournalInformation Sciences
Publication statusPublished - 1 Jan 2015


  • 3D object recognition
  • Hypergraph
  • Manifold learning
  • Multi-view fusion
  • Support vector machine

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence


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