南台科技大學 資訊工程系 Region partition and feature matching based color recognition of tongue image 指導教授：李育強 報告者 ：楊智雁 日期 ： 2010/04/19 Pattern Recognition Letters, Volume 28, Issue 1, Pages 11-19

2 Outline Introduction 1 LLE based sample outlier removal 2 Region partition in tongue image 3 Feature matching based on EMD metric 4 5Experiments and Conclusion

3 1. Introduction  TCM doctors have used information about the color, luster, shape  Dependent on the subjective experience and knowledge of the doctors  Color recognition of a tongue is very important for providing necessary information

4 1. Introduction (c.)  We have adopted the locally linear embedding (LLE) technique to remove the outliers among samples  A popular color–texture region segmentation method, namely JSEG  The segmented regions were matched to different categories of reference samples based on Earth Mover’s distance (EMD) metric

5 2. LLE based sample outlier removal  The colors in coatings are divided into gloom, light yellow, white and yellow classes

6 2. LLE based sample outlier removal (c.)  Then we analyze these samples with LLE and the result is visualized in a 2-D feature space  It can be found that the distribution of the embedding obtained by LLE exhibits a remarkable clustering tendency

7 2. LLE based sample outlier removal (c.)

8 3. Region partition in tongue image  The distribution of substances and coatings on the tongue surface is complex and diverse  If R falls into Part I, it will be classified into the coating class as long as 10% of votes have coating colors

9 3. Region partition in tongue image(c.) Region partition based on modified JSEG algorithm  Color quantization and spatial segmentation  Colors in the image are quantized to several representative classes  A criterion for good segmentation is applied to local windows in the class map

10 3. Region partition in tongue image(c.)

11 4. Feature matching based on EMD metric  Earth Mover’s distance (EMD) can compare histograms with different binnings  It is more robust in comparison to other histogram matching techniques  We adopt the EMD as a similarity measure between the tongue regions to be tested and the reference samples

12 4. Feature matching based on EMD metric(c.) Where fij is the optimal flow from xi to yj that minimizes

13 5. Experiments and Conclusion

14 5. Experiments and Conclusion(c.)

15 5. Experiments and Conclusion(c.)  An efficient method based on region partition and feature matching for color recognition of tongue images is presented  Using the LLE technique, we have removed the outliers among the reference samples collected  We have modified the JSEG method by replacing the original color quantization

16 5. Experiments and Conclusion(c.)  The feature matching scheme based on EMD  We have shown in the experiments that the proposed method was superior to the BP neural network classifier

南台科技大學 資訊工程系