Selecting suitable image retargeting methods with multi-instance multi-label learning

Muyang Song, Tongwei Ren, Yan Liu, Jia Bei, Zhihong Zhao

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Althogh the diversity of mobile devices brings in image retargeting technique to effectively display images on various screens, no existing image retargeting method can handle all images well. In this paper, we propose a novel approach to select suitable image retargeting methods solely based on original image characteristic, which can obtain acceptable selection accuracy with low computation cost. First, the original image is manually annotated with several simple features. Then, suitable methods are automatically selected from candidate image retargeting methods using multi-instance multi-label learning. Finally, target images are generated by the selected methods. Experiments demonstrate the effectiveness of the proposed approach.
Original languageEnglish
Title of host publicationBrain and Health Informatics - International Conference, BHI 2013, Proceedings
Pages418-426
Number of pages9
DOIs
Publication statusPublished - 1 Dec 2013
EventInternational Conference on Brain and Health Informatics, BHI 2013 - Maebashi, Japan
Duration: 29 Oct 201331 Oct 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8211 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Brain and Health Informatics, BHI 2013
Country/TerritoryJapan
CityMaebashi
Period29/10/1331/10/13

Keywords

  • Image characteristic analysis
  • Image retargeting
  • Method selection
  • Multi-instance multi-label learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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