A Novel Framework for Estimating Viewer Interest by Unsupervised Multimodal Anomaly Detection
A reliable method to estimate viewer interest is highly sought after for human-centered video information retrieval. A method that estimates viewer interest while users are watching Web videos is presented in this paper. The method uses a framework for anomaly detection based on collaborative use of...
Main Authors: | Yuma Sasaka, Takahiro Ogawa, Miki Haseyama |
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Format: | Article |
Language: | English |
Published: |
IEEE
2018-01-01
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Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8289336/ |
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