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Two-Dimensional Linear Prediction Model-Based Decorrelation Method
June 1989 (vol. 11 no. 6)
pp. 661-665
A unified feature extraction scheme, the two-dimensional (2-D) linear prediction model-based decorrelation method, is presented. By applying 2-D causal linear prediction model to decorrelate a textured image, the very heavy computation load required when using a whitening operator to decorrelate the image, or the significant information loss when using the gradient operator to approximately whiten the image is avoided. The texture model-based decorrelation provides three sets of features to perform texture classification: the coefficients of the 2-D linear prediction, the moments of error residuals and the autocorrelation values. An optimum feature-selection scheme using modified branch-and-bound method was introduced to reduce information redundancy. After feature selection, 100% classification accuracy was achieved for a 20-class texture problem. Experiments show that this feature extraction scheme is truly information lossless, effective, and fast.
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Index Terms:
2D linear prediction model based decorrelation; picture processing; pattern recognition; feature extraction; textured image; whitening operator; texture classification; error residuals; autocorrelation values; feature-selection; branch-and-bound method; correlation methods; filtering and prediction theory; pattern recognition; picture processing
Citation:
Z. Lin, Y. Attikiouzel, "Two-Dimensional Linear Prediction Model-Based Decorrelation Method," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 6, pp. 661-665, June 1989, doi:10.1109/34.24801
