Soh Ling Min

University of Surrey

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Josef Kittler

University of Surrey

Siqi Wang

Hebei Normal University

Jichao Chen

Nanjing University of Chinese Medicine

Guang-Bin Huang

Zhengzhou University

Yijie Zeng

Qilu University of Technology

Jiřı́ Matas

Czech Technical University in Prague

Cooperative Institutions

Signal Processing (United States)

Nanyang Technological University

Institute of Electrical and Electronics Engineers

University Town of Shenzhen

University of Surrey

Jiangnan University

Group Image (Poland)

Nanjing University of Science and Technology

Chinese Academy of Sciences

Universidade de Brasília

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Empirical evaluation of a calibration chart detector

Machine Vision and Applications (2001)

Soh Ling Min Josef Kittler Jiřı́ Matas

10.1007/s001380050148

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Citations (6)

Octree-based Convolutional Autoencoder Extreme Learning Machine for 3D Shape Classification

2022 International Joint Conference on Neural Networks (IJCNN) (2018)

Jichao Chen Yijie Zeng Siqi Wang Soh Ling Min Guang-Bin Huang

We introduce Octree-based Convolutional Autoencoder Extreme Learning Machine (OCA-ELM) for 3D shape classification. This approach combines Convolutional Autoencoder Extreme Learning Machine (CAE-ELM) with octreebased con- volution to generate feature maps from several types of geometric data, and extract discriminative features with Extreme Learning Machine Autoencoder (ELM-AE). The extracted features can then be used for various computer graphics applications, such as 3D shape classification. Compared with other 3D classification methods, the proposed OCA-ELM has superior classification performance. Experiments on ModelNet40 show that OCA-ELM outperforms state-of-the-art CNN-based methods and surpasses CAE-ELM in classification accuracy by 3.69%, demonstrating the effectiveness of our method.

Autoencoder

Extreme Learning Machine

Discriminative model

Octree

Feature (linguistics)

Contextual image classification

10.1109/ijcnn.2018.8489409

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Citations (7)