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, pp , Dec
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 Text-based Content-based
Keyword
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 Y component P1 P2 … Pj I component Q component
M: moment
N: total pixels
P: color value
i: ith component
j: jth pixel in i
h: total M in i
z: h×3
: weights for Y,I,Q
CT: feature vector
h=1, is mean of i component
h=2, is standard deviation of i component
(1×30,1×7.07)

7. Primitive 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. Primitive of the Image (2)
Clusters
CBj
CBj
CBj
PC1
CBj
CBj
CBj
PCk
CBj
PC2
CBj
CBj
cba,i
cba,i
cba,i
cba,i
cba,i
pc1,1
pc1,2
cba,i
pc1,z
cba,i
cba,i
cba,i
cba,i Y
e.g. 　
Block M1 M2
PC1
pc1,1 2 20 4 3 23 5
pc1,2 26 6 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
Match
Results

10. Relevance Feedback Algorithm
Proposed method
Color moment
Color set
Color correlograms
Dominant color
Color Layout
Color structure...
User Interface
Features
Database
Relevance Feedback
Algorithm
Image Database

11. Retrieval Results from D2 Database

12. Precision Comparison on D1 Database (1)
(a) The precision curves. (b) The precision vs. recall curves (T = 100)
N: number of relevant images retrieved
T: total number of relevant images
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.