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

页码：63 / 74

页数：12

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