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Feature Extraction of Video Using Artificial Neural Network
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Author(s): Yoshihiro Hayakawa (National College of Technology, Japan), Takanori Oonuma (National College of Technology, Japan), Hideyuki Kobayashi (National College of Technology, Japan), Akiko Takahashi (National College of Technology, Japan), Shinji Chiba (National College of Technology, Japan)and Nahomi M. Fujiki (National College of Technology, Japan)
Copyright: 2020
Pages: 17
Source title:
Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-0414-7.ch043
PurchaseView on the publisher's website for pricing and purchasing information.
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Abstract
In deep neural networks, which have been gaining attention in recent years, the features of input images are expressed in a middle layer. Using the information on this feature layer, high performance can be demonstrated in the image recognition field. In the present study, we achieve image recognition, without using convolutional neural networks or sparse coding, through an image feature extraction function obtained when identity mapping learning is applied to sandglass-style feed-forward neural networks. In sports form analysis, for example, a state trajectory is mapped in a low-dimensional feature space based on a consecutive series of actions. Here, we discuss ideas related to image analysis by applying the above method.
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