LCMKL: Latent-community and multi-kernel learning based image annotation

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6 Scopus citations

Abstract

Automatic image annotation is an important function for online photo sharing service. The concurrence of labels is pretty common in multi-label annotation. In this paper, we propose a novel approach called latent-community and multi-kernel learning (LCMKL). The established graph of labels is regarded as a semantic network. Community detection method is introduced that treats the label set as communities. Multi-kernel learning SVM is adopted for specifying communities and settling difficulty of extracting semantically meaningful entities with some simple features. Experiments on NUS-WIDE database demonstrate that LCMKL outperforms other state-of-the-art approaches.

Original languageEnglish
Title of host publicationCIKM 2013 - Proceedings of the 22nd ACM International Conference on Information and Knowledge Management
Pages1469-1472
Number of pages4
DOIs
StatePublished - 2013
Event22nd ACM International Conference on Information and Knowledge Management, CIKM 2013 - San Francisco, CA, United States
Duration: 27 Oct 20131 Nov 2013

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference22nd ACM International Conference on Information and Knowledge Management, CIKM 2013
Country/TerritoryUnited States
CitySan Francisco, CA
Period27/10/131/11/13

Keywords

  • Community detection
  • Image annotation
  • Multiple kernel learning

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