1. SVM Active Learning with Application to Image Retrieval
Simon Tong , Edward Chang,
Proceedings of the ninth ACM international conference on Multimedia,
September 30-October 05, 2001

2. Outline Introduction SVM Version Space Active Learning Experiments
Conclusion

3. Introduction- what is image retrieval?
An image retrieval system is a computer system for browsing, searching and retrieving images from a large database of digital images.
Most traditional methods of image retrieval utilize some method of adding metadata such as captioning, keywords, or descriptions to the images so that retrieval can be performed over the annotation words.
But as you known….
user is Lazy
So, Here is the Question …
How to automatically find the correct images for user ?

4. Introduction- relevance feedback
Because hand-labeling each image with descriptive words is time-consuming, and costly.
Thus, there is a need for a way to allow a user to implicitly inform a database of his or her desired output or query concept.
Relevance feedback can be used as a query refinement scheme to derive or learn a user’s query concept.
To solicit feedback, the refinement scheme displays a few image instances and the user labels each image as ‘relevant’ or ‘not relevant’.

5. Introduction- Active Learning
Based on the answers, another set of images from the database are brought up to the user for labeling.
The previous mentioned scheme often called pool-based active learning.
Label for relevant or not
image
image
image
image
image
image
image
image
image
image
image
image

6. Introduction- active learning
The main issue with active learning is finding a way to choose informative images within the pool to ask the user to label.
In general, and for the image retrieval task in particular, such a learner must meet two critical design goals.
The learner must learn target concepts accurately.
The learner must grasp a concept quickly, with only a small number of labeled instances, since most users do not wait around to provide a great deal of feedback
The key idea with active learning is that is should choose its next pool-query based on past answers to previous pool-queries

7. Introduction- proposed learner
In this study, we propose using a support vector machine active learner (short for SVMact) to achieve our goals.
The support vector machine active learner followed three idea below:
SVMact regards the task of learning a target concept as one of learning a SVM binary classifier.
SVMact learns the classifier quickly via active learning. The active part of SVMact selects the most informative instances with which to train the SVM classifier.
Once the classifier is trained, SVMact returns the top-k most relevant images.

8. SVM- Linear Classifier
f(x,w,b) = sign(w x + b)
denotes +1
denotes -1
w x + b>0
w x + b=0
How would you classify this data?
w x + b<0

9. SVM- Linear Classifier
f(x,w,b) = sign(w x + b)
denotes +1
denotes -1
How would you classify this data?

10. SVM- Linear Classifier
f(x,w,b) = sign(w x + b)
denotes +1
denotes -1
How would you classify this data?

11. SVM- Linear Classifier
f(x,w,b) = sign(w x + b)
denotes +1
denotes -1
Any of these would be fine..
..but which is best?

12. SVM- Linear Classifier
f(x,w,b) = sign(w x + b)
denotes +1
denotes -1
How would you classify this data?
Misclassified
to +1 class

13. SVM- Linear Classifier
f(x,w,b) = sign(w x + b)
f(x,w,b) = sign(w x + b)
denotes +1
denotes -1
denotes +1
denotes -1
Define the margin of a linear classifier as the width that the boundary could be increased by before hitting a datapoint.
Define the margin of a linear classifier as the width that the boundary could be increased by before hitting a datapoint.

14. SVM- Linear Classifier
Maximizing the margin is good
Implies that only support vectors are important; other training examples are ignorable.
Empirically it works very very well.
f(x,w,b) = sign(w x + b)
denotes +1
denotes -1
The maximum margin linear classifier is the linear classifier with the, maximum margin.
This is the simplest kind of SVM (Called an LSVM)
Support Vectors are those datapoints that the margin pushes up against
Linear SVM

15. SVM- SVM Mathematically
Classifier Hyper plane
Dist between Data point x to classifier

16. SVM- SVM Mathematically
Margin:
SVM want to maximize margin, so this is an object function
Subject to:
How to solve?
Lagrange Multiplier

17. SVM- Nonlinear dataset
If the data is not linearly separable, what should we do ?
The data is not linear separable in this space, doesn’t mean that it is still not linear separable in other space

18. Version Space- Notations
F: feature space
H: a set that contain all hypotheses (hyperplane)
W: parameter space
f(x): classifier (hyperplane)
v: version space
Version space is the set that contain all possible classifiers

19. Active Learning- concept
Given an unlabeled pool U , an active learner l has three components: l( f, q, X).
f : classifier (trained on the current set of labeled data X)
q: query component, which give a current labeled set X, decides which instance in U to query next.
X: labeled dataset
DEFINITION 4.1 Area(V) is the surface area that version space V occupies on the hypersphere, where ||w|| = 1
We want the classifier get more precise when queries rounds is increased. So we need to reduce the version space as much as possible

20. Active Learning- concept
LEMMA (Tong & Koller, 2000) suppose we have an input space X, finite dimensional feature space F (induced via a kernel K), and parameter space W. suppose active learner l* always queries instances whose corresponding hyperplanes in parameter space W halves the area of the current version space. Let l be any other active learner. Denote Vi respectively. Let P denote the set of all conditional distribution of y given x. then

21. Active Learning- concept
This discussion provide motivation for us an approach where we query instances that split the current version space into two equal parts as much as possible
Given an unlabeled instance X from the pool, it is not practical to explicitly compute the sizes of new space V-, V+.
Hence, there is a way of approximating this procedure
simple method: learn an SVM on existing labeled data and choose as the next instance to query the pool instance that comes closest to the hyperplane.

22. Active Learning- why simple work
The SVM unit vector w obtained from labeled data is the center of the largest hypersphere that can fit inside the current version space V.
The position of w is often approximately in the center of the version space.
So, we can test each of unlabeled instances x in the pool to see how close their corresponding hyperplane in W to the centrally placed w.
The distance calculation is straightforward

23. Active Learning- why simple work
labeled instance
unlabeled instance
Choose this for labeling w

24. Active Learning- SVMact summary
To summarize, our SVMact system performs the following for each round of relevance feedback:
Learn an SVM on the current labeled data
If this is the first feedback round, ask the user to label twenty randomly selected images. Otherwise, ask the user to label the twenty pool images closest to the SVM boundary.
After the relevance feedback rounds have been performed SVMact retrieves the top-k most relevant images:
Learn a final SVM on the labeled data
The final SVM boundary separate “relevant” images from irrelevant ones. Display the k relevant images that are farthest from the SVM boundary

25. Experiments- Image Characterization
Our image retrieval system employs a multi-resolution image representation scheme.
We characterize images by two main feature
Color
Texture
Given an image, we can extract the above color and texture information to produce a 144 dimensional vector of numbers.
Thus, the space X for our SVMs is a 144 dimensional space, and each image in our database corresponds to a point in this space.

26. Experiment- Image Databases
We used three real-world image dataset:
Four-category
Ten-category
Fifteen-category
Each category consisted 100 to 150 images
Architecture, flowers, landscape, people
Architecture, bears, clouds, flowers, landscape, people objectionable , tigers, tools, and waves
In addition to ten, Elephant, fabrics, fireworks, food, and texture

27. Experiments- method
The goal of SVMact is to learn a given concept through a relevance feedback process.
At each feedback round SVMact selects twenty images to ask the user to label as ‘relevant’ or ‘not relevant’ with respect to the query concept.
It then uses the labeled instances to successively refine the concept boundary.
After the relevance feedback rounds have finished SVMact then retrieves the top-k most relevant images from the dataset based on the final concept it has learned.

28. Experiments- average top-k accuracy

29. Experiments- compared with passive

30. Conclusion
Active learning with SVM can provide a powerful tool for searching image databases, outperforming a number of traditional query refinement schemes.
SVMact not only achieve s consistently high accuracy on a wide variety of desired returned results, but also does it quickly and maintains high precision when ask for large quantities of images.