Discriminant simplex analysis

Yun Fu, Shuicheng Yan, Thomas S Huang

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

Abstract

Image representation and distance metric are both significant for learning-based visual classification. This paper presents the concept of κ-Nearest-Neighbor Simplex (κNNS), which is a simplex with the vertices as the κ nearest neighbors of a certain point. κNNS contributes to the image classification problem in two aspects. First, a novel distance metric between a point to its κNNS within a certain class is provided for general classification problem. Second, we develop a new subspace learning algorithm, called Discriminant Simplex Analysis (DSA), to pursue effective feature representation for image classification. In DSA, the within-locality and between-locality are both modeled by κNNS distance, which provides a more accurate and robust measurement of the probability of a point belonging to a certain class. Experiments on real-world image classification demonstrate the effectiveness of both DSA as well as κNNS based classification approach.

Original languageEnglish (US)
Title of host publication2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Pages3333-3336
Number of pages4
DOIs
StatePublished - 2008
Event2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP - Las Vegas, NV, United States
Duration: Mar 31 2008Apr 4 2008

Other

Other2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
CountryUnited States
CityLas Vegas, NV
Period3/31/084/4/08

Keywords

  • Discriminant simplex analysis
  • Graph embedding
  • k-nearest-neighbor simplex
  • Subspace learning

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Fu, Y., Yan, S., & Huang, T. S. (2008). Discriminant simplex analysis. In 2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP (pp. 3333-3336). [4518364] https://doi.org/10.1109/ICASSP.2008.4518364