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Two-dimensional linear prediction model-based decorrelation method
Journal article   Open access   Peer reviewed

Two-dimensional linear prediction model-based decorrelation method

Z. Lin and Y. Attikiouzel
IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.11(6), pp.661-665
1989
DOI: https://doi.org/10.1109/34.24801
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Abstract

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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Citation topics
4 Electrical Engineering, Electronics & Computer Science
4.17 Computer Vision & Graphics
4.17.64 Content-Based Retrieval
Web Of Science research areas
Computer Science, Artificial Intelligence
Engineering, Electrical & Electronic
ESI research areas
Engineering
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