Affective abstract image classification and retrieval using multiple kernel learning

He Zhang, Zhirong Yang, Mehmet Gon̈en, Markus Koskela, Jorma Laaksonen, Timo Honkela, Erkki Oja

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

13 Scopus citations


Emotional semantic image retrieval systems aim at incorporating the user's affective states for responding adequately to the user's interests. One challenge is to select features specific to image affect detection. Another challenge is to build effective learning models or classifiers to bridge the so-called "affective gap". In this work, we study the affective classification and retrieval of abstract images by applying multiple kernel learning framework. An image can be represented by different feature spaces and multiple kernel learning can utilize all these feature representations simultaneously (i.e., multiview learning), such that it jointly learns the feature representation weights and corresponding classifier in an intelligent manner. Our experimental results on two abstract image datasets demonstrate the advantage of the multiple kernel learning framework for image affect detection in terms of feature selection, classification performance, and interpretation.

Original languageEnglish (US)
Title of host publicationNeural Information Processing - 20th International Conference, ICONIP 2013, Proceedings
Number of pages10
EditionPART 3
StatePublished - 2013
Externally publishedYes
Event20th International Conference on Neural Information Processing, ICONIP 2013 - Daegu, Korea, Republic of
Duration: Nov 3 2013Nov 7 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume8228 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other20th International Conference on Neural Information Processing, ICONIP 2013
Country/TerritoryKorea, Republic of


  • Group lasso
  • Image affect
  • Image classification and retrieval
  • Low-level image features
  • Multiple kernel learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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