Multi-Subspace Neural Network for Image Recognition

• URL: http://arxiv.org/abs/2006.09618v1
• Date: Wed, 17 Jun 2020 02:55:34 GMT
• Title: Multi-Subspace Neural Network for Image Recognition
• Authors: Chieh-Ning Fang, Chin-Teng Lin
• Abstract summary: In image classification task, feature extraction is always a big issue. Intra-class variability increases the difficulty in designing the extractors. Recently, deep learning has drawn lots of attention on automatically learning features from data. In this study, we proposed multi-subspace neural network (MSNN) which integrates key components of the convolutional neural network (CNN), receptive field, with subspace concept.
• Score: 33.61205842747625
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In image classification task, feature extraction is always a big issue. Intra-class variability increases the difficulty in designing the extractors. Furthermore, hand-crafted feature extractor cannot simply adapt new situation. Recently, deep learning has drawn lots of attention on automatically learning features from data. In this study, we proposed multi-subspace neural network (MSNN) which integrates key components of the convolutional neural network (CNN), receptive field, with subspace concept. Associating subspace with the deep network is a novel designing, providing various viewpoints of data. Basis vectors, trained by adaptive subspace self-organization map (ASSOM) span the subspace, serve as a transfer function to access axial components and define the receptive field to extract basic patterns of data without distorting the topology in the visual task. Moreover, the multiple-subspace strategy is implemented as parallel blocks to adapt real-world data and contribute various interpretations of data hoping to be more robust dealing with intra-class variability issues. To this end, handwritten digit and object image datasets (i.e., MNIST and COIL-20) for classification are employed to validate the proposed MSNN architecture. Experimental results show MSNN is competitive to other state-of-the-art approaches.

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