Feature Extraction of Video Using Artificial Neural Network

Yoshihiro Hayakawa, Takanori Oonuma, Hideyuki Kobayashi, Akiko Takahashi, Shinji Chiba and Nahomi M. Fujiki
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Yoshihiro Hayakawa: National College of Technology, Sendai, Japan
Takanori Oonuma: National College of Technology, Sendai, Japan
Hideyuki Kobayashi: National College of Technology, Sendai, Japan
Akiko Takahashi: Sendai National College of Technology, Sendai, Japan
Shinji Chiba: National College of Technology, Sendai, Japan
Nahomi M. Fujiki: National College of Technology, Sendai, Japan

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2017, vol. 11, issue 2, 25-40

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.

Date: 2017
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