Novel Gaussianized vector representation for improved natural scene categorization

Xi Zhou, Xiaodan Zhuang, Hao Tang, Mark Hasegawa-Johnson, Thomas S. Huang

Research output: Contribution to journalArticle

Abstract

We present a novel Gaussianized vector representation for scene images by an unsupervised approach. Each image is first encoded as an ensemble of orderless bag of features. A global Gaussian Mixture Model (GMM) learned from all images is then used to randomly distribute each feature into one Gaussian component by a multinomial trial. The posteriors of the feature on all the Gaussian components serve as the parameters of the multinomial distribution. Finally, the normalized means of the features distributed in every Gaussian component are concatenated to form a supervector, which is a compact representation for each scene image. We prove that these supervectors observe the standard normal distribution. The Gaussianized vector representation is a more generalized form of the widely used histogram representation. Our experiments on scene categorization tasks using this vector representation show significantly improved performance compared with the histogram-of-features representation. This paper is an extended version of our work that won the IBM Best Student Paper Award at the 2008 International Conference on Pattern Recognition (ICPR 2008) (Zhou et al., 2008).

Original languageEnglish (US)
Pages (from-to)702-708
Number of pages7
JournalPattern Recognition Letters
Volume31
Issue number8
DOIs
StatePublished - Jun 1 2010

Keywords

  • Expectation-Maximization
  • Gaussian mixture model
  • Image classification
  • Unsupervised learning

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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