Recognition of Film Type Using HSV Features on Deep-Learning Neural Networks

被引：2
作者：
Lu C.-T. [1 ]
Chang C.-Y. [1 ]
Liu C.-H. [1 ]
Wang L.-L. [1 ]
Lin J.-A. [2 ]
Tseng K.-F. [3 ]
机构：
[1] Department of Information Communication, Asia University, Taichung
[2] Department of Digital Media Design, Asia University, Taichung
[3] School of Electronics and Communication Engineering, Quanzhou University of Information Engineering, Quanzhou
来源：
Journal of Electronic Science and Technology | 2020年 / 18卷 / 01期
关键词：
Deep-learning; film type recognition; hue; saturation; and brightness value (HSV) analysis; neural networks; video classification;
D O I：
10.11989/JEST.1674-862X.90904223
中图分类号：
学科分类号：
摘要：
The number of films is numerous and the film contents are complex over the Internet and multimedia sources. It is time consuming for a viewer to select a favorite film. This paper presents an automatic recognition system of film types. Initially, a film is firstly sampled as frame sequences. The color space, including hue, saturation, and brightness value (HSV), is analyzed for each sampled frame by computing the deviation and mean of HSV for each film. These features are utilized as inputs to a deep-learning neural network (DNN) for the recognition of film types. One hundred films are utilized to train and validate the model parameters of DNN. In the testing phase, a film is recognized as one of the five categories, including action, comedy, horror thriller, romance, and science fiction, by the trained DNN. The experimental results reveal that the film types can be effectively recognized by the proposed approach, enabling the viewer to select an interesting film accurately and quickly. © 2020
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页码：31 / 41
页数：10
相关论文
共 21 条
  • [1] Andrej K., George T., Sanketh S., Thomas L., Rahul S., Li F.-F., Large-scale video classification with convolutional neural networks, Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp. 1725-1732, (2014)
  • [2] Bovik A. C., Handbook of Image and Video Processing, (2010)
  • [3] de Avila S. E. F., Lopes A. P. B., da Luz A., de Albuquerque Araujo A., VSUMM: A mechanism designed to produce static video summaries and a novel evaluation method, Pattern Recognition Letters, 32, 1, pp. 56-68, (2011)
  • [4] Giannetti L. D., Understanding Movies, (1996)
  • [5] Dimitrova N., Agnihotri L., Wei G., Video classification based on HMM using text and faces, Proc. of European Conf. on Signal Processing, pp. 1-4, (2000)
  • [6] Naphide H. R., Huang T.-S., A probabilistic framework for semantic video indexing, filtering, and retrieval, IEEE Trans. on Multimedia, 3, 1, pp. 141-151, (2001)
  • [7] Furini M., Geraci F., Montangero M., Pellegrini M., STIMO: Still and moving video storyboard for the web scenario, Multimedia Tools and Applications, 46, 1, (2010)
  • [8] Kang H.-B., Affective content detection using HMMs, Proc. of the 11th ACM Intl. Conf. on Multimedia, Berkeley, pp. 259-262, (2013)
  • [9] Zettl H., Sight Sound Motion: Applied Media Aesthetics, (2013)
  • [10] Krizhevsky A., Sutskever I., Hinton G. E., ImageNet classification with deep convolutional neural networks, Proc. of the 25th Intl. Conf. on Neural Information Processing Systems, pp. 1-9, (2012)
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