Movie genre classification: A multi-label approach based on convolutions through time
| Autor: | Jonatas Wehrmann, Rodrigo C. Barros |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Contextual image classification
Computer science business.industry 020207 software engineering 02 engineering and technology computer.software_genre Machine learning Convolutional neural network Film genre Task (project management) Image (mathematics) ComputingMethodologies_PATTERNRECOGNITION 0202 electrical engineering electronic engineering information engineering Feature (machine learning) 020201 artificial intelligence & image processing Artificial intelligence Layer (object-oriented design) business computer Software Natural language processing |
| Zdroj: | Applied Soft Computing. 61:973-982 |
| ISSN: | 1568-4946 |
| DOI: | 10.1016/j.asoc.2017.08.029 |
| Popis: | The task of labeling movies according to their corresponding genre is a challenging classification problem, having in mind that genre is an immaterial feature that cannot be directly pinpointed in any of the movie frames. Hence, off-the-shelf image classification approaches are not capable of handling this task in a straightforward fashion. Moreover, movies may belong to multiple genres at the same time, making movie genre assignment a typical multi-label classification problem, which is per se much more challenging than standard single-label classification. In this paper, we propose a novel deep neural architecture based on convolutional neural networks (ConvNets) for performing multi-label movie-trailer genre classification. It encapsulates an ultra-deep ConvNet with residual connections, and it makes use of a special convolutional layer to extract temporal information from image-based features prior to performing the mapping of movie trailers to genres. We compare the proposed approach with the current state-of-the-art methods for movie classification that employ well-known image descriptors and other low-level handcrafted features. Results show that our method substantially outperforms the state-of-the-art for this task, improving classification performance for all movie genres. |
| Databáze: | OpenAIRE |
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