COMPUTER GRAPHICS CLASSIFICATION BASED ON MARICOV PROCESS MODEL AND BOOSTING FEATURE SELECTION TECHNIQUE

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.