TY - GEN
T1 - Computer graphics classification based on Markov process model and boosting feature selection technique
AU - Sutthiwan, Patchara
AU - Cai, Xiao
AU - Shi, Yun Q.
AU - Zhang, Hong
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
KW - Boosting feature selection
KW - Computer graphics classification
KW - Markov process
UR - http://www.scopus.com/inward/record.url?scp=77951957656&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77951957656&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2009.5413344
DO - 10.1109/ICIP.2009.5413344
M3 - Conference contribution
AN - SCOPUS:77951957656
SN - 9781424456543
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 2913
EP - 2916
BT - 2009 IEEE International Conference on Image Processing, ICIP 2009 - Proceedings
PB - IEEE Computer Society
T2 - 2009 IEEE International Conference on Image Processing, ICIP 2009
Y2 - 7 November 2009 through 10 November 2009
ER -