Multimodal learning for multi-label image classification

Yanwei Pang, Zhao Ma, Yuan Yuan, Xuelong Li, Kongqiao Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

Abstract

We tackle the challenge of web image classification using additional tags information. Unlike traditional methods that only use the combination of several low-level features, we try to use semantic concepts to represent images and corresponding tags. At first, we extract the latent topic information by probabilistic latent semantic analysis (pLSA) algorithm, and then use multi-label multiple kernel learning to combine visual and textual features to make a better image classification. In our experiments on PASCAL VOC'07 set and MIR Flickr set, we demonstrate the benefit of using multimodal feature to improve image classification. Specifically, we discover that on the issue of image classification, utilizing latent semantic feature to represent images and associated tags can obtain better classification results than other ways that integrating several low-level features.

Original languageEnglish
Title of host publicationICIP 2011
Subtitle of host publication2011 18th IEEE International Conference on Image Processing
Pages1797-1800
Number of pages4
DOIs
StatePublished - 2011
Externally publishedYes
Event2011 18th IEEE International Conference on Image Processing, ICIP 2011 - Brussels, Belgium
Duration: 11 Sep 201114 Sep 2011

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Conference

Conference2011 18th IEEE International Conference on Image Processing, ICIP 2011
Country/TerritoryBelgium
CityBrussels
Period11/09/1114/09/11

Keywords

  • Multilabel learning
  • Multimodal features
  • Multiple kernel learning

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