A novel Gaussianized vector representation for natural scene categorization

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

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

Abstract

This paper presents a novel Gaussianized vector representation for scene images by an unsupervised approach. First, each image is encoded as an ensemble of orderless bag of features, and then a global Gaussian Mixture Model (GMM) learned from all images is used to randomly distribute each feature into one Gaussian component by a multinomial trial. The parameters of the multinomial distribution are defined by the posteriors of the feature on all the Gaussian components. 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 super-vectors observe the standard normal distribution. Our experiments on scene categorization tasks using this vector representation show significantly improved performance compared with the bag-of-features representation.

Original languageEnglish (US)
Title of host publication2008 19th International Conference on Pattern Recognition, ICPR 2008
StatePublished - Dec 1 2008
Event2008 19th International Conference on Pattern Recognition, ICPR 2008 - Tampa, FL, United States
Duration: Dec 8 2008Dec 11 2008

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Other

Other2008 19th International Conference on Pattern Recognition, ICPR 2008
CountryUnited States
CityTampa, FL
Period12/8/0812/11/08

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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  • Cite this

    Zhou, X., Zhuang, X., Tang, H., Hasegawa-Johnson, M., & Huang, T. S. (2008). A novel Gaussianized vector representation for natural scene categorization. In 2008 19th International Conference on Pattern Recognition, ICPR 2008 [4761665] (Proceedings - International Conference on Pattern Recognition).