Learn concepts in multiple-instance learning with diverse density framework using supervised mean shift

Ruo Du, Sheng Wang, Qiang Wu, Xiang Jian He

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

1 Citation (Scopus)

Abstract

Many machine learning tasks can be achieved by using Multiple-instance learning (MIL) when the target features are ambiguous. As a general MIL framework, Diverse Density (DD) provides a way to learn those ambiguous features by maxmising the DD estimator, and the maximum of DD estimator is called a concept. However, modeling and finding multiple concepts is often difficult especially without prior knowledge of concept number, i.e., every positive bag may contain multiple coexistent and heterogeneous concepts but we do not know how many concepts exist. In this work, we present a new approach to find multiple concepts of DD by using an supervised mean shift algorithm. Unlike classic mean shift (an unsupervised clustering algorithm), our approach for the first time introduces the class label to feature point and each point differently contributes the mean shift iterations according to its label and position. A feature point derives from an MIL instance and takes corresponding bag label. Our supervised mean shift starts from positive points and converges to the local maxima that are close to the positive points and far away from the negative points. Experiments qualitatively indicate that our approach has better properties than other DD methods.

Original languageEnglish
Title of host publicationProceedings - 2010 Digital Image Computing
Subtitle of host publicationTechniques and Applications, DICTA 2010
Pages643-648
Number of pages6
DOIs
Publication statusPublished - 2010
Externally publishedYes
EventInternational Conference on Digital Image Computing: Techniques and Applications, DICTA 2010 - Sydney, NSW, Australia
Duration: 1 Dec 20103 Dec 2010

Publication series

NameProceedings - 2010 Digital Image Computing: Techniques and Applications, DICTA 2010

Conference

ConferenceInternational Conference on Digital Image Computing: Techniques and Applications, DICTA 2010
Country/TerritoryAustralia
CitySydney, NSW
Period1/12/103/12/10

Keywords

  • Diverse density
  • Mean shift
  • MIL
  • Supervised

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications

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