Convolutional Neural Network Based Multi-feature Fusion for Non-rigid 3D Model Retrieval

Hui Zeng, Yanrong Liu, Siqi Li, JianYong Che and Xiuqing Wang
Volume: 14, No: 1, Page: 176 ~ 190, Year: 2018
10.3745/JIPS.04.0058
Keywords: Convolutional Neural Network, HKS, Multi-Feature Fusion, Non-rigid 3D Model, WKS
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Abstract
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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Cite this article
IEEE Style
Hui Zeng, Yanrong Liu, Siqi Li, JianYong Che, and Xiuqing Wang, "Convolutional Neural Network Based Multi-feature Fusion for Non-rigid 3D Model Retrieval," Journal of Information Processing Systems, vol. 14, no. 1, pp. 176~190, 2018. DOI: 10.3745/JIPS.04.0058.

ACM Style
Hui Zeng, Yanrong Liu, Siqi Li, JianYong Che, and Xiuqing Wang, "Convolutional Neural Network Based Multi-feature Fusion for Non-rigid 3D Model Retrieval," Journal of Information Processing Systems, 14, 1, (2018), 176~190. DOI: 10.3745/JIPS.04.0058.