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1 October 2018 Multimodal social media video classification with deep neural networks
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Proceedings Volume 10808, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2018; 108082U (2018) https://doi.org/10.1117/12.2501679
Event: Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2018, 2018, Wilga, Poland
Abstract
Classifying videos according to their content is a common task across various contexts, as it allows effective content tagging, indexing and searching. In this work, we propose a general framework for video classification that is built on top of several neural network architectures. Since we rely on a multimodal approach, we extract both visual and textual features from videos and combine them in a final classification algorithm. When trained on a dataset of 30 000 social media videos and evaluated on 6 000 videos, our multimodal deep learning algorithm outperforms shallow single-modality classification methods by a large margin of up to 95%, achieving overall accuracy of 88%.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Tomasz Trzcinski "Multimodal social media video classification with deep neural networks", Proc. SPIE 10808, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2018, 108082U (1 October 2018); https://doi.org/10.1117/12.2501679
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