Group sparse ensemble learning for visual concept detection

Yongqing Sun, Kyoko Sudo, Yukinobu Taniguchi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

To exploit the hidden group structures of data and thus detect concepts in videos, this paper proposes a novel group sparse ensemble learning approach based on Automatic Group Sparse Coding (AutoGSC). We first adopt AutoGSC to learn both a common dictionary over different data groups and an individual group-specific dictionary for each data group which can help us to capture the discrimination information contained in different data groups. Next, we represent each data instance by using a sparse linear combination of both dictionaries. Finally, we propose an algorithm to use the reconstruction errors of data instances to calculate the ensemble gating function for ensemble construction and fusion. Experiments on the TRECVid 2008 benchmark show that the ensemble learning proposal achieves promising results and outperforms existing approaches.

Original languageEnglish
Title of host publicationAdvances in Multimedia Information Processing, PCM 2013 - 14th Pacific-Rim Conference on Multimedia, Proceedings
PublisherSpringer Verlag
Pages649-657
Number of pages9
ISBN (Print)9783319037301
DOIs
Publication statusPublished - 1 Jan 2013
Event14th Pacific-Rim Conference on Multimedia, PCM 2013 - Nanjing, China
Duration: 13 Dec 201316 Dec 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8294 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th Pacific-Rim Conference on Multimedia, PCM 2013
CountryChina
CityNanjing
Period13/12/1316/12/13

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Keywords

  • Ensemble learning
  • Semantic indexing
  • Sparse representation
  • Visual concept detection

Cite this

Sun, Y., Sudo, K., & Taniguchi, Y. (2013). Group sparse ensemble learning for visual concept detection. In Advances in Multimedia Information Processing, PCM 2013 - 14th Pacific-Rim Conference on Multimedia, Proceedings (pp. 649-657). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8294 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-03731-8_60