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

作     者：Ching-Ta Lu Jia-An Lin Chia-Yi Chang Chia-Hua Liu Ling-Ling Wang Kun-Fu Tseng Ching-Ta Lu;Jia-An Lin;Chia-Yi Chang;Chia-Hua Liu;Ling-Ling Wang;Kun-Fu Tseng

作者机构：Department of Information CommunicationAsia UniversityTaichung 41354 the Department of Digital Media DesignAsia UniversityTaichung 41354 School of Electronics and Communication EngineeringQuanzhou University of Information EngineeringQuanzhou 362000 

出 版 物：《Journal of Electronic Science and Technology》 (电子科技学刊（英文版）)

年 卷 期：2020年第18卷第1期

页      面：31-41页

核心收录：

学科分类：12[管理学] 1303[艺术学-戏剧与影视学] 13[艺术学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by MOST under Grant No.MOST 104-2221-E-468-007 

主　　题：Deep-learning film type recognition hue,saturation,and brightness value(HSV)analysis neural networks video classification 

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.