Computer graphics classification based on Markov process model and boosting feature selection technique

Patchara Sutthiwan, Xiao Cai, Yun Q. Shi, Hong Zhang

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

15 Scopus citations

Abstract

In this paper, a novel technique is proposed to identify computer graphics by employing second-order statistics to capture the significant statistical difference between computer graphics and photographic images. Due to the wide availability of JPEG images, a JPEG 2-D array formed from the magnitudes of quantized block DCT coefficients is deemed a feasible input; however, a difference JPEG 2-D array tells a better story about image statistics with less influence from image content. Characterized by transition probability matrix (TPM), Markov process, widely used in digital image processing, is applied to model the difference JPEG 2-D arrays along horizontal and vertical directions. We resort to a thresholding technique to reduce the dimensionality of feature vectors formed from TPM. YCbCr color system is selected because of its demonstrated better performance in computer graphics classification than RGB color system. Furthermore, only Y and Cb components are utilized for feature generation because of the high correlation found in the features derived from Cb and Cr components. Finally, boosting feature selection technique is used to greatly reduce the dimensionality of features without sacrificing the machine learning based classification performance.

Original languageEnglish (US)
Title of host publication2009 IEEE International Conference on Image Processing, ICIP 2009 - Proceedings
PublisherIEEE Computer Society
Pages2913-2916
Number of pages4
ISBN (Print)9781424456543
DOIs
StatePublished - Jan 1 2009
Event2009 IEEE International Conference on Image Processing, ICIP 2009 - Cairo, Egypt
Duration: Nov 7 2009Nov 10 2009

Publication series

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

Other

Other2009 IEEE International Conference on Image Processing, ICIP 2009
CountryEgypt
CityCairo
Period11/7/0911/10/09

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

Keywords

  • Boosting feature selection
  • Computer graphics classification
  • Markov process

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