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Conference Paper
4D Effect Video Classification with Shot-aware Frame Selection and Deep Neural Networks
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- Authors
- Thomhert S. Siadari, Mikyong Han, Hyunjin Yoon
- Issue Date
- 2017-10
- Citation
- International Conference on Computer Vision Workshops (ICCVW) 2017, pp.1148-1155
- Language
- English
- Type
- Conference Paper
- Abstract
- A 4D effect video played at cinema or other designated places is a video annotated with physical effects such as motion, vibration, wind, flashlight, water spray, and scent. In order to automate the time-consuming and labor-intensive process of creating such videos, we propose a new method to classify videos into 4D effect types with shot-aware frame selection and deep neural networks (DNNs). Shot-aware frame selection is a process of selecting video frames across multiple shots based on the shot length ratios to subsample every video down to a fixed number of frames for classification. For empirical evaluation, we collect a new dataset of 4D effect videos where most of the videos consist of multiple shots. Our extensive experiments show that the proposed method consistently outperforms DNNs without considering multi-shot aspect by up to 8.8% in terms of mean average precision.
- KSP Keywords
- Deep neural network(DNN), Empirical Evaluation, Frame selection, Multi-shot, Video classification, Water spray, mean average precision, new method, physical effects, video frames