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Conference Paper Shot Category Detection based on Object Detection Using Convolutional Neural Networks
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Authors
Deokkyu Jung, Jeong-Woo Son, Sun-Joong Kim
Issue Date
2018-02
Citation
International Conference on Advanced Communications Technology (ICACT) 2018, pp.36-39
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.23919/ICACT.2018.8323638
Abstract
Shot category detection is a method that extracts classified shots which comprised of several sequenced frames. These are based on object detection, which relates feature extraction of image processing. Shot category detection is helpful to utilize the neural network. Neural networks are widely generalized to our society. Artificial neural networks applied in many kinds of researches and developments, which provide convenient technologies for fundamental knowledge of deep learning. In terms of artificial neural networks, we introduce shot category detection based on object detection using convolutional neural networks (CNN). This paper also proves that CNN is efficient for supervised learning.
KSP Keywords
Artificial Neural Network, Convolution neural network(CNN), Feature extractioN, Image processing, Object detection, Researches and developments, Supervised Learning, category detection, deep learning(DL)