Multi-instance learning with an extended kernel density estimation for object categorization

Ruo Du, Qiang Wu, Xiangjian He, Jie Yang

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

Abstract

Multi-instance learning (MIL) is a variational supervised learning. Instead of getting a set of instances that are labeled, the learner receives a set of bags that are labeled. Each bag contains many instances. In this paper, we present a novel MIL algorithm that can efficiently learn classifiers in a large instance space. We achieve this by estimating instance distribution using a proposed extended kernel density estimation (eKDE) which is an alternative to previous diverse density estimation (DDE). A fast method is devised to approximately locate the multiple modes of eKDE. Comparing to DDE, eKDE is more efficient and robust to the labeling noise (the mislabeled training data). We compare our approach with other state-of-the-art MIL methods in object categorization on the popular Caltech-4 and SIVAL datasets, the results illustrate that our approach provides superior performance.

Original languageEnglish
Title of host publicationProceedings of the 2012 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2012
Pages477-482
Number of pages6
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event2012 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2012 - Melbourne, VIC, Australia
Duration: 9 Jul 201213 Jul 2012

Publication series

NameProceedings of the 2012 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2012

Conference

Conference2012 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2012
Country/TerritoryAustralia
CityMelbourne, VIC
Period9/07/1213/07/12

Keywords

  • extended kernel density estimation
  • mean shift
  • multi-instance learning
  • object categorization

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction

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