1. Improvement of Content-Based Image Retrieval
by Using Clustering and Relevance Feedback
Master Thesis Defense
Bui The Vinh
May 13, 2010

2. Content
 Introduction
 Image’s Features & Similarity
 Clustering Algorithm
 Relevance Feedback
 Implementation and Evaluation
 Conclusions and Future Work
2

3. Introduction
3
 Key points
 How to represent an image
 How to determine whether two images are similar or not
 Framework

4. Introduction
4
 Practical Applications
 Medical diagnosis
 Crime prevention
 Online shopping
 Etc.
 Challenges
 Real-time system
 High accuracy
 Contributions
 Build a complete CBIR
system
 Improve the searching time
by using clustering
 Increase the accuracy by
applying support vector
machine in Relevance
Feedback

5. Content
 Introduction
 Image Features & Similarity
 Clustering Algorithm
 Relevance Feedback
 Implementation and Evaluation
 Conclusions and Future Work
5

6. Feature Extraction Model
6
F1
B
F2
F3
 Basic Image features: COLOR, SHAPE, TEXTURE

7. Image Representation
7
 Image representation
 CEDD: Color and edge
directivity descriptor (proposed
by Chatzichristofis and
Boutalis)
 Incorporate color and texture
information in a histogram
 Each image is represented by
a high dimensional real vector
0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0
0 0 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0
0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
 Vectors representing images depend on the method of extracting image
features

8. Similarity Measurement
8
 Formula
 Calculate the distance between two corresponding vectors
 Tanimoto distance
F1
F3
F2

9. Content
Introduction
Image’s Features
Clustering Algorithm
Relevance Feedback
Implementation and Evaluation
Conclusions and Future Work
9

10. Overview of Clustering
10
 Motivation
 The amount of image data involved is very large
 Finding groups of objects such that:
 The objects in a group will be similar to one another
 The objects in a group will be different from the objects in other groups

11. K-means Clustering
11
 Definition
 K-means is a partition clustering algorithm based on iterative relocation that
partitions a dataset into k clusters.
 Objective
 Locally minimizes sum of squared distance between the data points and
their corresponding cluster centers:
 Given a set of observations (x1, x2, …, xn);
Cluster into k sets (k < n) X = {X1, X2, …, Xk}

12. K-means Clustering (2)
12
 Algorithm
 Initialize k cluster centers randomly. Repeat until it converges:
 Cluster Assignment Step: Assign each data point xi to the cluster fh such
that distance of xi from center of fh is minimum
 Center Re-estimation Step: Re-estimate each cluster center as the mean
of the points in that cluster

13. Content
Introduction
Image’s Features
Clustering Algorithm
Relevance Feedback
Implementation and Evaluation
Conclusions and Future Work
13

14. Relevance feedback?
14
 Motivation
 The limitation of low-level image feature-based searching
 Mechanism
 After initial retrieval results are presented, allow the user to provide
feedback on the relevance of one or more of the retrieved images.
 Use this feedback information to reformulate the query.
 Produce new results based on reformulated query.
 Challenges
 Require real-time processing
 Training data set is small

15. RF Architecture
15
Rankings
CBIR
System
Ranked
Images
1. Img1
2. Img2
3. Img3
.
.
1. Img1 
2. Img2 
3. Img3 
.
.
Feedback
Query
Image
Revised
Query
Re-Ranked
Images
1. Img2
2. Img4
3. Img5
.
.
Query
Reformulation
Images
Database

16. Support vector machine
16
 Classification method
 Given a set of training examples, each marked as belonging to one of two
categories
 An SVM training algorithm builds a model that predicts whether a new
example falls into one category or the other.
 Linear Case
 Training data
 A separating hyperplane
 Optimal separating hyperplane (OSH)

17. Support vector machine (2)
17
 Linear Case (cont.)
 The classification function
 Non-linear Case
 The classification function
 Kernels

18. Content
Introduction
Image’s Features
Clustering Algorithm
Relevance Feedback
Implementation and Evaluation
Conclusions and Future Work
18

19. Clustering Implementation
19
 Clustering
 Take feature vectors database
as input
 Apply K-means algorithm to
cluster the database
 Finding
 Find appropriate cluster with
the query image

20. RF Implementation
20
 Support vector machine classifier
 Suitable when number of training data is
small
 Can be applied in a real-time system

21. Environment & Parameters
21
 Environment
 9918 images with various
kinds of images
 Desktop computer: Intel
Core 2 Dual 3.16 GHz, 4-GB
RAM, Windows 7 Ultimate
 Sun Java 1.6-u7
 All components of the
system are implemented by
using Java
 Parameters
 Choose K=7 for K-means
algorithm
 Choose radical basis function
(RBF) for support vector
machine

22. Clustering Evaluation
22
 Accuracy
 Clustering does not adversely affect the accuracy

23. Clustering Evaluation
23
 Searching time
Applying clustering
significantly improves the
performance

24. RF Evaluation
24
 Accuracy
 Improve the accuracy after several iterations

25. Content
Introduction
Image’s Features
Clustering Algorithm
Relevance Feedback
Implementation and Evaluation
Conclusions and Future Work
25

26. Conclusion
26
 Achievements
 Successfully build a complete content-based image retrieval system
 The performance is significantly improved by applying K-means clustering
algorithm to cluster image database
 Using support vector machine in “Relevance Feedback” can remarkably
increase the accuracy
 Shortcomings
 Low-level feature-based searching method depends on other authors’
method
 Future works
 Develop a low-level feature-based searching method that is suitable with
each kind of images domain

27. 27