Poster-Based Multiple Movie Genre Classification Using Inter-Channel Featuresopen access
- Authors
- Wi, Jeong A.; Jang, Soojin; Kim, Young Bin
- Issue Date
- Apr-2020
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Motion pictures; Machine learning; Feature extraction; Databases; Task analysis; Image color analysis; Data mining; Classification; dataset; deep learning; gram matrix; multi-label classification; movie genre classification; movie poster
- Citation
- IEEE ACCESS, v.8, pp 66615 - 66624
- Pages
- 10
- Journal Title
- IEEE ACCESS
- Volume
- 8
- Start Page
- 66615
- End Page
- 66624
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/40800
- DOI
- 10.1109/ACCESS.2020.2986055
- ISSN
- 2169-3536
- Abstract
- As the scale of the film industry grows, the demand for well-established movie databases is also growing. The genre of a movie supplies information on its overall content and has multiple values. Therefore, it should be well classified utilizing the characteristics of movies, without omissions in the database. In this study, we extract the optimal information and characteristics from movie posters to aid the classification of movies into genres and propose the use of a Gram layer in a convolutional neural network (CNN). The Gram layer first extracts style features by applying the Gram matrix to produce a feature map of a poster image. Using this as a style weight, the existing feature map is merged with style information to perform the genre classification task. The proposed Gram layer performed multi-genre classification tasks with higher efficiency than a residual neural network (ResNet), which is the current CNN model used for such tasks. We compared the activation map with the Squeeze-and-Excitation network, which gives weight to the image, and we confirmed that the introduction of the Gram layer actually focuses on the style of the movie poster. To classify the movie genres, we reconstructed the poster dataset into 12 multi-genres that emphasized the characteristics of each poster.
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- Appears in
Collections - Graduate School of Advanced Imaging Sciences, Multimedia and Film > Department of Imaging Science and Arts > 1. Journal Articles
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