Crowdsourced time-sync video tagging using semantic association graph

Time-sync comments reveal a new way of extracting the online video tags. However, such time-sync comments have lots of noises due to users' diverse comments, introducing great challenges for accurate and fast video tag extractions. In this paper, we propose an unsupervised video tag extraction...

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Bibliographic Details
Main Authors: Wenmian Yang, Na Ruan, Wenyuan Gao, Kun Wang, Wensheng Ran, Weijia Jia
Format: Conference Proceeding
Language:English
Subjects:
Clustering algorithms
crowdsourced time-sync comments
Feature extraction
Image edge detection
keywords extraction
semantic association graph
Semantics
Streaming media
Tagging
video tagging
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Summary:Time-sync comments reveal a new way of extracting the online video tags. However, such time-sync comments have lots of noises due to users' diverse comments, introducing great challenges for accurate and fast video tag extractions. In this paper, we propose an unsupervised video tag extraction algorithm named Semantic Weight-Inverse Document Frequency (SW-IDF). SW-IDF first generates corresponding semantic association graph (SAG) using semantic similarities and timestamps of the time-sync comments. Then it clusters the comments into sub-graphs of different topics and assigns weight to each comment based on SAG. This can clearly differentiate the meaningful comments with the noises. In this way, the noises can be identified, and effectively eliminated. Extensive experiments have shown that SW-IDF can achieve 0.3045 precision and 0.6530 recall in high-density comments; 0.3800 precision and 0.4460 recall in low-density comments. It is the best performance among the existing unsupervised algorithms.
ISSN:1945-788X
DOI:10.1109/ICME.2017.8019364