An EM based multiple instance learning method for image classification

Hsiao-Tien Pao*, S. C. Chuang, Y. Y. Xu, Hsin Chia Fu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


In this paper, we propose an EM based learning algorithm to provide a comprehensive procedure for maximizing the measurement of diverse density on given multiple Instances. Furthermore, the new EM based learning framework converts an MI problem into a single-instance treatment by using EM to maximize the instance responsibility for the corresponding label of each bag. To learn a desired image class, a user may select a set of exemplar images and label them to be conceptual related (positive) or conceptual unrelated (negative) images. A positive image consists of at least one object that the user may be interested, and a negative image should not contain any object that the user may be interested. By using the proposed EM based learning algorithm, an image retrieval prototype system is implemented. Experimental results show that for only a few times of relearning cycles, the prototype system can retrieve user's favor images from WWW over Internet.

Original languageAmerican English
Pages (from-to)1468-1472
Number of pages5
JournalExpert Systems with Applications
Issue number3
StatePublished - 1 Oct 2008


  • EM method
  • Image retrieve
  • Multiple-instance learning
  • WWW

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