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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
南台科技大學
資訊工程系
Outline
2
1
Introduction
2
LLE based sample outlier removal
3
Region partition in tongue image
4
Feature matching based on EMD metric
5
Experiments and Conclusion
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
3
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
4
2. LLE based sample outlier removal
 The colors in coatings are divided into gloom, light
yellow, white and yellow classes
5
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
6
2. LLE based sample outlier removal (c.)
7
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
8
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
9
3. Region partition in tongue image(c.)
10
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
11
4. Feature matching based on EMD metric(c.)
 
EMD( P, Q) 
 
m
i 1
m
i 1
n
j 1 ij ij
n
j 1 ij
f d
f
Where fij is the optimal flow from xi to yj that minimizes
p  {( x1 , w1R ),....., ( xm , wmR )}
Q  {( y1 , w1s ),....., ( yn , wns )}
12
5. Experiments and Conclusion
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5. Experiments and Conclusion(c.)
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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
15
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
16
南台科技大學
資訊工程系