Dried jujube classification using support vector machine based on fractal parameters and red, green and blue intensity
Version of Record online: 15 JUN 2012
© 2012 The Authors. International Journal of Food Science and Technology © 2012 Institute of Food Science and Technology
International Journal of Food Science & Technology
Volume 47, Issue 9, pages 1951–1957, September 2012
How to Cite
Lou, H., Hu, Y., Wang, B. and Lu, H. (2012), Dried jujube classification using support vector machine based on fractal parameters and red, green and blue intensity. International Journal of Food Science & Technology, 47: 1951–1957. doi: 10.1111/j.1365-2621.2012.03055.x
- Issue online: 13 AUG 2012
- Version of Record online: 15 JUN 2012
- (Received 30 October 2011; Accepted in revised form 9 March 2012)
Figure S1. Three grades of dried jujube fruits appearance (A) and contour plots of cross validation accuracy for C-SVM models based on fractal dimensions (B), fractal measures (C), fractal dimensions and fractal measures (D), RGB intensity (E), fractal dimensions and RGB intensity (F), fractal measures and RGB intensity (G), and fractal dimensions, fractal measures and RGB intensity (H) when optimizing the penalty constant C and the RBF kernel parameter gamma (γ).
Figure S2. The images and their corresponding threshold images at the intensity range 0–30 of three grades of dried jujube fruits: grade 1 (A), grade 2 (B), grade 3 (C).
Figure S3. Fractal analysis of chosen image.
Table S1. The three fractal dimensions (FD) and three fractal measures (FM) for black (FDBBW, FMBBW), white (FDWBW, FMWBW), partially black and white areas (FDBW, FMBW) and RGB intensity values derived from dried jujube fruits.
Table S2. The number of support vectors (nSVM) and the accuracy rate of classification on the test set.
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