1. Color Image Retrieval based on Primitives of Color Moments
J.-L. Shih, L.-H. Chen, IEE Proceeding-Vision, Image and Signal Processing, Vol. 149 No. 6, Dec. 2002, pp
Advisor：Prof. Chang, Chin-Chen
Student：Chen, Yan-Ren
Date：2003/03/25

2. Outlines Introduction Proposed Method
Extraction of Primitives of Color Moments
Color Image Retrieval
Relevance Feedback Algorithm
Experimental Results
Conclusions

3. Introduction ...... ...... Image Retrieval Methods Text-based
Content-based
Keyword
Text
Description
Color
Shape
......
Color Histogram
Color Moments
......

4. Proposed Method Flowchart
Extract Features
(Primitives)
Query Image
Similarity
Measure
Matched
Results
Image Database
Relevance
Feedback
Algorithm
Features Database

5. Extraction of Primitives of Color Moments
Image
Divide
Image
Y, I, Q Color Space
Extract Primitives
Cluster
Color
Moments
Extract Color Moments

6. Color Moments Q component I component Pj … P2 P1 Y component
h=1, is mean of i component
h=2, is standard deviation of i component
M: moment
N: total pixels
P: color value
i: ith component
j: jth pixel in i
h: total M in i
: weights for Y,I,Q
CT: feature vector
M: moment
N: total pixels
P: color value
i: ith component
j: jth pixel in i
h: total M in i
: weights for Y,I,Q
CT: feature vector
CT=[ct1,ct2,..,ct6]=
=[Y(1×30,1×7.07), I(2×10,2×2), Q(1.5×20,1.5×4)]

7. Primitives of the Image (1)
Y(1×30,1×7)
I(2×10,2×2)
Q(1.5×20,1.5×4) a
M: moment
i: ith component
H: total M in i
z: H×3
: weights for Y,I,Q
a: ath block of the image
CB: feature vector

8. Primitives of the Image (2)
CB1
CB2
CB3
CB4
Clusters
CB2
PC1
CB4
CB3
CB1
PC2
Extract Central Vector Examples Y
Block a M1 M2
PC1
(pc1,1, pc1,2)
=(21.5, 4.5)
a=2 20 4
a=3 23 5
PC2
(pc2,1, pc2,2)
=(28, 6.5)
a=4 26 6
a=1 30 7
Weight=1, Threshold=5
M: moment
H: total M in i
z: H×3
k: kth cluster
n: size of kth cluster
J:1,2,...,nk
a: ath block of the image
CB: feature vector
PC: primitive (central vector)

9. Color Image Retrieval – Similarity Measure
Query Image
Features
Distance
calculate
Minimum
Distance
Features in Database
Matched
Results

10. Relevance Feedback Algorithm
Proposed method
Color moments
Color set
Color correlograms
Dominant color
Color layout
Color structure
Color histogram...
User Interface
Features
Database
Relevance Feedback
Algorithm
Image Database
1.System give query results by combined features.
2.User choices r similar images.
3.According user response, R.F.A choices query method.

11. Retrieval Results from D2 Database

12. Precision Comparison on D1 Database (1)
N: number of relevant images retrieved
K: total number of retrieved images

13. Precision Comparison on D1 Database (2)
K=50, T=100

14. Precision Comparison on D2 Database

15. Conclusions
Proposed a image retrieval method based on primitives of color moments.
The color moments of all blocks are extracted and clustered.
Central vectors are considered as primitives (Feature vectors).
Similarity measure is used to perform color image retrieval.
Relevance feedback algorithm determines the most appropriate feature.