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- 저자 : ( Jianyong Che ) (검색결과 2 건)
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Convolutional Neural Network Based Multi-feature Fusion for Non-rigid 3D Model Retrieval
( Hui Zeng ),( Yanrong Liu ),( Siqi Li ),( Jianyong Che ),( Xiuqing Wang ) 한국정보처리학회 2018 Journal of information processing systems Vol.14 No.1
This paper presents a novel convolutional neural network based multi-feature fusion learning method for nonrigid 3D model retrieval, which can investigate the useful discriminative information of the heat kernel signature (HKS) descriptor and the wave kernel signature (WKS) descriptor. At first, we compute the 2D shape distributions of the two kinds of descriptors to represent the 3D model and use them as the input to the networks. Then we construct two convolutional neural networks for the HKS distribution and the WKS distribution separately, and use the multi-feature fusion layer to connect them. The fusion layer not only can exploit more discriminative characteristics of the two descriptors, but also can complement the correlated information between the two kinds of descriptors. Furthermore, to further improve the performance of the description ability, the cross-connected layer is built to combine the low-level features with high-level features. Extensive experiments have validated the effectiveness of the designed multi-feature fusion learning method.
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Convolutional Neural Network Based Multi-feature Fusion for Non-rigid 3D Model Retrieval
Zeng, Hui,Liu, Yanrong,Li, Siqi,Che, JianYong,Wang, Xiuqing Korea Information Processing Society 2018 Journal of information processing systems Vol.14 No.1
This paper presents a novel convolutional neural network based multi-feature fusion learning method for non-rigid 3D model retrieval, which can investigate the useful discriminative information of the heat kernel signature (HKS) descriptor and the wave kernel signature (WKS) descriptor. At first, we compute the 2D shape distributions of the two kinds of descriptors to represent the 3D model and use them as the input to the networks. Then we construct two convolutional neural networks for the HKS distribution and the WKS distribution separately, and use the multi-feature fusion layer to connect them. The fusion layer not only can exploit more discriminative characteristics of the two descriptors, but also can complement the correlated information between the two kinds of descriptors. Furthermore, to further improve the performance of the description ability, the cross-connected layer is built to combine the low-level features with high-level features. Extensive experiments have validated the effectiveness of the designed multi-feature fusion learning method.
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