Multimedia Tools and Applications

, Volume 65, Issue 1, pp 63-74

First online:

Open Access This content is freely available online to anyone, anywhere at any time.

Relational large scale multi-label classification method for video categorization

  • Wojciech IndykAffiliated withWroclaw University of Technology
  • , Tomasz KajdanowiczAffiliated withWroclaw University of Technology
  • , Przemyslaw KazienkoAffiliated withWroclaw University of Technology Email author 


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.


Multi-label classification Relational learning MapReduce Classification in networks Automated video categorization Automated video tagging Cloud computing Parallel computing