Relational large scale multi-label classification method for video categorization

被引：16
|
作者：
Indyk, Wojciech [1 ]
Kajdanowicz, Tomasz [1 ]
Kazienko, Przemyslaw [1 ]
机构：
[1] Wroclaw Univ Technol, PL-50370 Wroclaw, Poland
来源：
MULTIMEDIA TOOLS AND APPLICATIONS | 2013年 / 65卷 / 01期
关键词：
Multi-label classification; Relational learning; MapReduce; Classification in networks; Automated video categorization; Automated video tagging; Cloud computing; Parallel computing;
D O I：
10.1007/s11042-012-1149-2
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：63 / 74
页数：12
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