Film Classification Using Deep Learning on Color Properties

• Main Authors: LIU, CHIA-HUA, 劉佳樺
• Other Authors: LU, CHING-TA
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/b876py

碩士 === 亞洲大學 === 資訊傳播學系 === 106 === The number of films is numerous and complex. A viewer wants to choose a favorite movie is time consuming. This thesis aims to develop automatic film classification systems. The proposed methods allow viewers to determine the type of films they want to watch without first viewing the trailer or the video content. Firstly, a film is sampled frame by frame. The color space including red, green, blue, yellow, hue, saturation, and brightness value (HSV) in each frame is analyzed. The mean and deviation of the HSV are computed and utilized as classification features for each film. These features are employed in the proposed rule-oriented classification method and also fed into deep learning neural networks. The trailers are classified into five categories, including science fiction, literature-love, action, comedy films, and horror thrillers. In order to evaluate the effectiveness of the classification, we use the precision rate, recall rate, and F-measure as objective measures. Experimental results show that literature-love and comedy films are mostly red and yellow, science fiction films and horror thrillers are mostly green and blue, and action and adventure Dong-yang films are mostly yellow and red, while western films are mostly blue and green. By using the rule-oriented classification method, the accuracy, recall rates, and the F measure are 83.33%, 100%, and 90.71%, respectively. We also use deep learning neural networks for film classification, with a precision rate of 93.3%, a recall rate of 92%, and an F-measure of 91.4%. The performance is satisfied.