1 July 2008 Multivariate statistical projection methods to perform robust feature extraction and classification in surface grading
Author Affiliations +
J. of Electronic Imaging, 17(3), 031106 (2008). doi:10.1117/1.2957886
We present an innovative way to simultaneously perform feature extraction and classification for the quality-control issue of surface grading by applying two multivariate statistical projection methods: SIMCA and PLS-DA. These tools have been applied to compress the color texture data that describe the visual appearance of surfaces (soft color texture descriptors) and to directly perform classification using statistics and predictions from the projection models. Experiments have been carried out using an extensive ceramic images database (VxC TSG) comprised of 14 different models, 42 surface classes, and 960 pieces. A factorial experimental design evaluated all the combinations of several factors affecting the accuracy rate. These factors include the tile model, color representation scheme (CIE Lab, CIE Luv, and RGB), and compression/classification approach (SIMCA and PLS-DA). Moreover, a logistic regression model is fitted from the experiments to compute accuracy estimates and study the effect of the factors on the accuracy rate. Results show that PLS-DA performs better than SIMCA, achieving a mean accuracy rate of 98.95%. These results outperform those obtained in a previous work where the soft color texture descriptors in combination with the CIE Lab color space and the k-NN classifier achieved an accuracy rate of 97.36%.
Jose M. Prats-Montalban, Fernando Lopez, Jose M. Valiente, Alberto Ferrer, "Multivariate statistical projection methods to perform robust feature extraction and classification in surface grading," Journal of Electronic Imaging 17(3), 031106 (1 July 2008). http://dx.doi.org/10.1117/1.2957886

RGB color model

Statistical modeling


Data modeling

Principal component analysis

Feature extraction

Error analysis

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