1. Content-based image retrieval using growing hierarchical self-organizing quadtree map
Source: Pattern Recognition Vol. 38, May,
2005, pp
Author: Sitao Wu, M. K. M. Rahman, Tommy
W. S. Chow
Reporter: Yung-Chen Chou
Date: Mar. 10, 2005

2. Outline Introduction JSEG Region Segmentation
Self-Organizing Maps (SOM)
Growing Hierarchical SOM (GHSOM)
Proposed Method
Experimental Results
Conclusions
Yung-Chen Chou

3. Introduction Image index database Feature extraction Feature
Similarity
matching
Query image
Basic concept of image retrieval
Yung-Chen Chou

4. Introduction CBIR types: Region segmentation (JSEG) SOM and GHSOM
Histogram extraction
Color layout extraction
Region-based extraction
Region segmentation (JSEG)
SOM and GHSOM
Feedback adjustment
Yung-Chen Chou

5. Introduction
Architecture of GHSOQM-based CBIR system
Yung-Chen Chou

6. JSEG region segmentation (Deng et al. 1999)
Original image
J-image
Region-based image
Schematic of the JSEG algorithm
Yung-Chen Chou

7. Self-Organizing Maps (Kohonen 1980s)
Yung-Chen Chou

8. Self-Organizing Maps (Kohonen 1980s)
Learning rate
Yung-Chen Chou

9. Growing Hierarchical Self-Organizing Maps (Rauber 2002)
Yung-Chen Chou

10. Proposed method
Color
standard
deviation
Texture
standard
deviation
={f1, f2, f3, f4, f5, f6, f7, f8, f9, f10, f11, f12, f13}
JSEG
Lab color
texture
The region
Percentage
of an image
Image x
(Lab color space) f1 f2 …
f13 :
Feature matrix for image x
Yung-Chen Chou

11. Neighborhood function
Proposed method
Neighborhood function
Learning rate
Yung-Chen Chou

12. Proposed method
Query image
Image x
Yung-Chen Chou

13. Proposed method Y Z Q Y’ Z’
Yung-Chen Chou

14. Experimental results 1000 images and classify to ten classes
The sizes of the images are 384X256, or 256X384
τ = 20; λ=20;
Time comparison:
GHSOQM
SIMPLIcity
Training (sec)
1890
No need
Query time (sec)
3.08
7.25
Yung-Chen Chou

15. Experimental results
The concept diagram of SIMPLIcity
Yung-Chen Chou

16. Experimental results
Yung-Chen Chou

17. Experimental results
Yung-Chen Chou

18. Conclusions
Using a neural network to organize images into a hierarchical structure
The query time is largely reduced
Do not need retraining all images when adding the new images
No discussion about memory use
Yung-Chen Chou