1. Image Retrieval Based on Regions of Interest
Source : KNOWLEDGE AND DATA ENGINEERING,
IEEE Vol.: 15, No. 4, JULY,2003 pp
Author : Khanh Vu, Kien A. Hua, Senior Member,
Wallapak Tavanapong
Reporter : Shing-Shoung Wang
Date : 2005/5/3

2. Outline Introduction Retrieval Procedure Experimental Study
Conclusions

3. Introduction
QBE(Query-By-Example) is the most widely supported method.
Existing CBIR(Content-Based Image Retrieval) systems for ROI(region-of-interest).
1.not effective.
2.color histograms disadvantages.

4. Retrieval Procedure Q Image Signature Image Signature
A Similarity Model for ROI Queries
Clustering&
Indexing
Image Signature

5. Retrieval Procedure(Cont.)
Sample block
16×16 pixels
Sampling rate
256 pixels
256 pixels

6. Retrieval Procedure(Cont.)
Handling the Scaling of the Matching Objects.
database images
query images

7. Retrieval Procedure(Cont.)
Image Signatures:
Apply 7 pairs of mean-variance vector as
Image Signatures.
((μ1, σ12),(μ2, σ22),(μ3, σ32),(μ4, σ42),(μ5, σ52),(μ6, σ62),(μ7, σ72))
Core Area
upper 1 2
lower 3 4

8. Retrieval Procedure(Cont.)
Clustering and Indexing:
Images signatures are enormous for large
data sets.
1.map the signatures of each image into signature points, and cluster them into mininal bounding retangles(MBR).
2.R*-tree.

9. Retrieval Procedure(Cont.)
A Similarity Model for ROI Queries
SamMatch Environment.
Munsell color system.
Similarity Measure
Wi a weight factor
Wi = q ⋅|c/2 - ci|
the distance between the color of block i of subimage Q&S

10. Retrieval Procedure(Cont.)
Ranking Retrieved Images:
1. determine ROI in the image, those that fall
within the boundary of S;
2. extract these blocks from the 113 blocks of
the image—they constitute the feature vector of
the subimage S to be compared; and
3. perform block-to-block comparison to determine
the similarity of Q and S according to (1).

11. Experimental Study Comparative Studies
Metric. Let A1, A2, . . ., Aq denote the q relevant images in response to a query Q. The recall R is defined for a scope S, S > 0, as:

12. Experimental Study(Cont.)
3 Types of NFQs(Noise Free Querys)
Type 1:
The query image has the same size as those in the database. The queried object covers only a small region of the query image.
Type 2:
The query image has the same size as those in the database. The query is relatively large, covering almost the entire query image.
Type 3:
The query image is smaller or larger than the size of the database images.

13. Experimental Study(Cont.)
Performance Issues Specific to SamMatch
R/S averages under specific types of NFQs. (a) Under type-1 NFQs, (b) under type-2 NFQs, and (c) under type-3 NFQs.
Corr.:Correlogram SM:SamMatch LCH:Local Color Histogram

14. Experimental Study(Cont.)
Performance Issues Specific to SamMatch

15. Conclusions When retrievaling in large image data sets. 1.ROI queries.
2.Fast retrieval.
3.Different sizes.