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• W2083670761 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." @default.
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• W2083670761 date "2015-11-01" @default.
• W2083670761 modified "2023-10-17" @default.
• W2083670761 title "Multi-view ensemble manifold regularization for 3D object recognition" @default.
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• W2083670761 doi "https://doi.org/10.1016/j.ins.2015.03.032" @default.
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