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Relational large scale multi-label classification method for video categorization
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DOI：10.1007/s11042-012-1149-2.png)
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摘要
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The problem of automated video categorization in large datasets is considered in the paper. A new Iterative Multi-label Propagation (IMP) algorithm for relational learning in multi-label data is proposed. Based on the information of the already categorized videos and their relations to other videos, the system assigns suitable categories-multiple labels to the unknown videos. The MapReduce approach to the IMP algorithm described in the paper enables processing of large datasets in parallel computing. The experiments carried out on 5-million videos dataset revealed the good efficiency of the multi-label classification for videos categorization. They have additionally shown that classification of all unknown videos required only several parallel iterations.
Keyword:
Multi-label classification
Relational learning
MapReduce
Classification in networks
Automated video categorization
Automated video tagging
Cloud computing
Parallel computing
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
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