ASTS: attention based spatio-temporal sequential framework for movie trailer genre classification
| Autor: | Yang Li, Ziyu Lu, Delong Liu, Yitong Yu |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
Multimedia search Computer Networks and Communications Computer science business.industry Trailer 020207 software engineering 02 engineering and technology computer.software_genre MovieLens Task (project management) Feature (linguistics) Hardware and Architecture 0202 electrical engineering electronic engineering information engineering Media Technology Key (cryptography) Artificial intelligence Representation (mathematics) business computer Software Natural language processing |
| Zdroj: | Multimedia Tools and Applications. 80:9749-9764 |
| ISSN: | 1573-7721 1380-7501 |
| DOI: | 10.1007/s11042-020-10125-y |
| Popis: | Automatic movie trailer genre classification is a challenging task because trailers have more diverse content and high-level sequential semantic concepts within the movie storyline, which can help for multimedia search and personalized movie recommendation. Traditional methods generally extract the low-level features or consider the local sequential dependencies among trailer frames, ignoring the global high-level sequential semantic concepts. In this manuscript, we propose a novel and effective Attention based Spatio-temporal Sequential Framework (ASTS) for movie trailer genre classification. The proposed framework mainly consists of two modules, respectively the spatio-temporal descriptive module and the attention-based sequential module. The spatio-temporal descriptive module adopts some advanced convolution neural networks to extract the spatio-temporal features of key trailer frames, which can capture the local spatio-temporal semantic features. The attention-based sequential module is designed to process the extracted spatio-temporal feature representation sequence for capturing the global high-level sequential semantic concepts within the movie storyline. We crawl 14,415 labeled movie trailers from YouTube and integrate them into the public dataset MovieLens. Experiment results show that our proposed framework is superior to state-of-the-art methods. |
| Databáze: | OpenAIRE |
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