Multimedia Tools and Applications

, Volume 75, Issue 20, pp 12627–12644 | Cite as

Multi-view multi-label learning for image annotation

  • Fuhao Zou
  • Yu Liu
  • Hua Wang
  • Jingkuan Song
  • Jie Shao
  • Ke Zhou
  • Sheng Zheng
Article

Abstract

Image annotation is posed as multi-class classification problem. Pursuing higher accuracy is a permanent but not stale challenge in the field of image annotation. To further improve the accuracy of image annotation, we propose a multi-view multi-label (abbreviated by MVML) learning algorithm, in which we take multiple feature (i.e., view) and ensemble learning into account simultaneously. By doing so, we make full use of the complementarity among the views and the base learners of ensemble learning, leading to higher accuracy of image annotation. With respect to the different distribution of positive and negative training examples, we propose two versions of MVML: the Boosting and Bagging versions of MVML. The former is suitable for learning over balanced examples while the latter applies to the opposite scenario. Besides, the weights of base learner is evaluated on validation data instead of training data, which will improve the generalization ability of the final ensemble classifiers. The experimental results have shown that the MVML is superior to the ensemble SVM of single view.

Keywords

Image Annotation Ensemble learning Multi-view learning Multi-label learning 

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Fuhao Zou
    • 1
  • Yu Liu
    • 2
  • Hua Wang
    • 2
  • Jingkuan Song
    • 3
  • Jie Shao
    • 4
  • Ke Zhou
    • 2
  • Sheng Zheng
    • 2
  1. 1.School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhanChina
  2. 2.Wuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhanChina
  3. 3.Department of Information Engineering and Computer ScienceUniversity of TrentoTrentoItaly
  4. 4.School of Computer Science and TechnologyUniversity of Electronic Science and Technology of ChinaWuhanChina

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