1. Fuzzy Color Histogram and Its Use in Color Image retrieval
Author: Ju Han and Kai-Kuang Ma
Source: IEEE Trans. Image Processing, Vol. 11, No. 8, pp , 2002
Date: 2004/4/21
Adviser: Dr. Chin-Chen Chang
Speaker: Hao-Yu Lo
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2. Outline Conclusions The conventional color histogram (CCH)
The proposed method
fuzzy color histogram(FCH)
Experimental results
(CCH)和(FCH)的目的都是做image retrieval。
Color histogram在這篇paper被視為顏色分佈的機率。
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3. Conclusions
A new color histogram representation called fuzzy color histogram (FCH)was proposed.
FCH is less sensitive than CCH on dealing with lighting intensity changes and region of interest image retrieval.
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4. conventional color histogram(CCH)
H(I)=[h1, h2, …, hn], where hi=Ni /N
(R,G,B)
image I b r w y o g
用CCH表示一張圖，H(I)=[h1, h2, …, hn]。
N: total pixels in image I。n: color bins in color space。hi=Ni /N
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5. fuzzy color histogram(FCH)
F(I)=[f1, f2, …, fc], where
input pixels
fuzzy c-mean(FCM)
cluster algorithm fi
pixels x x x x4………….. xN
ij
membership ………….. 0.5
value of f1
Pj指的就是，從image I裡選出來的第j個pixel，(據理解，每個pixel被選到的機率都是1/16)。
ij指的是，第j個pixel在第i個bin的隸屬度。
membership ………… 0.4
value of f2 . . . . . .
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6. Example of fuzzy c-mean(FCM) cluster algorithm(1/3)
Initialize two cluster’s center v1=(166,169,189)
v2=(120,123,124)
t=1
X1=(186, 185, 197)
X2=(162, 142, 165)
X3=(206, 208, 209)
X4=(142, 131, 160) f1
11=0.4
12 =0.7
1 3 =0.2
14 =0.6 f2
21 =0.6
22 =0.3
23 =0.8
24 =0.4
ij i j
(1/||xj-vi||2)1/(m-1)
, i=1,2; j=1,2,3,4
( ) 1
1/2-1
11=
( )
( ) 1
1/2-1 1
1/2-1
f1(1)=( )/4, f2(1)=( )/4
F(1)=(1.9/4, 2.1/4)
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7. Example of fuzzy c-mean(FCM) cluster algorithm(2/3)
Compute new two cluster’s center v1(1)=(163,165,168)
v2 (1) =(118,121,125) N
ij j j N
ij j
t=2
X1=(186, 185, 197)
X2=(162, 142, 165)
X3=(206, 208, 209)
X4=(142, 131, 160) f1
11=0.5
12 =0.67
1 3 =0.26
14 =0.55 f2
21 =0.5
22 =0.33
23 =0.74
24 =0.45
f1(2)=( )/4, f2(2)=( )/4
F(2)=(1.98/4, 2.02/4)
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8. Example of fuzzy c-mean(FCM) cluster algorithm(3/3)
Iteration until ||F(t)-F(t-1)||<,stop. else, t=t+1
f1(5)=( )/4=1.99/4,
f2(5)=( )/4=2.01/4
F(5)=(1.99/4, 2.01/4)
f1(6)=( )/4=2/4,
f2(6)=( )/4=2/4
F(6)=(2/4, 2/4)
stop.
v1(6)=(140,162,171), v2 (6)=(185,193,204)
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9. fuzzy c-mean(FCM) weighting exponent m, and error tolerance .
Step1) Input the number of clusters c, the
weighting exponent m, and error tolerance .
Step2) Initialize the cluster centers V={vi}, for 1  i  c.
Step3) Input data X={x1, x2, …,xN}.
Step4) Calculate the c cluster centers V={vi (t)}.
Step5) Update new F={f1, f2,…, fc,}
Step6) If ||F(t)-F(t-1)||<,stop. else, t=t+1,return to step 4
有效計算 FCH的方法，是一個架構在fuzzy c-mean(FCM)的clustering演算法。介紹fuzzy c-mean在幹嘛。
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10. Experimental results(1/2)
vary lighting intensity from
–25, -20, ……,+20, +25
解釋rank的定義。
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11. Experimental results(2/2)
retrieved images by using CCH
retrieved images by using FCH
解釋為什麼要做regional retrieval。
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retrieved images by using CCH
retrieved images by using FCH