Laplacian affinity propagation for semi-supervised object classification

Yun Fu, Zhu Li, Xi Zhou, Thomas S. Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We solve the semi-supervised multi-class object classification problem by a graph-based learning algorithm, called Laplacian Affinity Propagation (LAP). The idea is to model and train both labeled and unlabeled data by constructing a local neighborhood affinity graph in a smoothness formulation of Laplacian matrix, based on graph mincuts or harmonic energy minimization. The unknown labels for unlabeled data are inferred from an optimized graph embedding procedure subject to the labeled data. Such label-to-unlabel propagation scheme can provide a closed form solution via a learning framework that is flexible for any new design. LAP integrates embedding and classifier together and gives smooth labels with respect to the underlying manifold structure formed by the training data. Object classification experiments on COIL database demonstrate the effectiveness and applicability of such algorithm.

Original languageEnglish (US)
Title of host publication2007 IEEE International Conference on Image Processing, ICIP 2007 Proceedings
PublisherIEEE Computer Society
Pages189-192
Number of pages4
ISBN (Print)1424414377, 9781424414376
DOIs
StatePublished - 2006
Event14th IEEE International Conference on Image Processing, ICIP 2007 - San Antonio, TX, United States
Duration: Sep 16 2007Sep 19 2007

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume1
ISSN (Print)1522-4880

Other

Other14th IEEE International Conference on Image Processing, ICIP 2007
CountryUnited States
CitySan Antonio, TX
Period9/16/079/19/07

Keywords

  • Graph embedding
  • Local affinity
  • Object classification
  • Semi-supervised learning
  • Spectral clustering

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Fu, Y., Li, Z., Zhou, X., & Huang, T. S. (2006). Laplacian affinity propagation for semi-supervised object classification. In 2007 IEEE International Conference on Image Processing, ICIP 2007 Proceedings (pp. 189-192). [4378923] (Proceedings - International Conference on Image Processing, ICIP; Vol. 1). IEEE Computer Society. https://doi.org/10.1109/ICIP.2007.4378923