1. Visual Learning with Navigation as an Example
Dr Juyang Weeng
Dr Shaoyun Chen
Michigan Sate University

2. Model Based Methods PROS Efficient for predictable cases
Easier to understand
Computationally inexpensive
CONS
Non generic
Not able to deal with every possible case
Potentially huge number of exhaustive cases.
Talk about Model based methods
Specify each model
If the environment is not steady /restrictive doesn’t work.

3. Example of Model based learning
[1]
Applicability checker. Checks if the models specified can handle it

4. MODEL FREE METHODS Yt+1=f(Xt) Automatically learn the model
Xt input image in rc dimensional space(S)
Yt+1 control signal in space C
The image needs to be vectorized.
GOAL :Approximate the function f
Yt+1=f(Xt)
Mapping Space S to C with a function

5. Recursion Partition Tree
Each leaf node represents sample(X,Y)
Each node represents a set of data points with increased similarity
One of the central ideas in Shoslif’s approach
Given X find f(X) at the corresponding leaf node after traversal.
Characterizes the distribution while building tree

6. Learning Phase Building a Regression Partition Tree
Take the sample space S.
Divide the space into b cells. Each a child of the root.
The analysis performs automatic derivation of features(discussed later).
Continue to do this until the leaf nodes have a single data point or many data points with virtually the same Y.

7. How to construct the RPT Learning Phase2 9 1 1 7 6 2 3 4 5 3 4 6 7 5 8 8 9

8. Performance phase Input X’ Output Y control signal
Recursively analyze the centre of each node
If it is close to the input then proceed in that direction till you reach the leaf node .
Use the corresponding Control signal
Use top k paths to find the top k nearest centers.

9. Automatic Feature Derivation
Feature Selection :Select features from a set of human defined features.
Feature Extraction: extrapolates selected features from images
Feature Derivation : derives features from high dimensional vector inputs
Using Principal Component Analysis recursively partitions the space S into a subspace S’ where the training samples lie.
Write rules for deriving features

10. PCA Computes the principal component vectors .
V1,V2,V3,V4…..VN
MEF : Most Expressive Features
They explain the variation in the sample set
The hyper plane that has V1 as a normal an that passes through the centroid of the samples forms a partition.
The samples on one side fall onto on side of the tree and vice versa.

11. PCA v/s LDA
[1]
PCA
LDA

12. LDA We can do better with class information.
MDF :Most discriminating feature
Similar to PCA
This method is cuts more along the class boundaries.
Differences
MEF: samples spread out widely, and the samples of different classes tend to mix together.
MDF: samples are clustered more tightly, and the samples from different classes are farther apart.

13. Using States Using a model similar to Markov chain model
St State at time t
At time t, the system is at state St and observes image Xt.
Control vector Yt+1 and enters the next state St+1.
(St+1, Yt+1) = f (St, Xt)

14. The Observation driven Markov Model[1]

15. Dealing with local attention
A special state A (ambiguous) indicates that local visual attention is needed.
Eg. trainer defined this state for a segment right before a turn.
If the image area that revealed the visual difference between different turn types was mainly in a small part of the scene.
A directs the system to look at such landmarks through a prespecified image sub window so that the system
issues the correct steering action before it is too late.
our objective is to train the system to actively pay attention to critical parts of the
scene—landmarks—according to each situation, and to disregard unrelated parts in the
scene. For learning-based methods, using landmarks is not trivial. We must systematically
incorporate the attention selection mechanism into the learning system instead
of explicitly programming it into a navigation program

16. Incremental Learning
Batch learning : All the training data are available at the time the system learns.
Incremental learning : Training samples are available only one at a time.
Discard once you have used them
Memory requires to store the image only once.
Similar images discarded

17. The Learning Process Step 1 Query the current RPT. Step 2
If the difference between the current RPT’s output and the desired control signal >prespecified tolerance.
Go to Step 3 Else Goto Step 1
Step 3
Shoslif learns the current sample to update the RPT.

18. Shoslif versus other methods
Compared Shoslif with feed forward neural networks and radial basis function networks for approximating stateless appearance- based navigation systems.
Shoslif did significantly better than both methods.
Extension to face detection, speech recognition and vision-based robot arm action learning.

19. Conclusion Shoslif performs better in benign scenes.
The state based method allows more flexibility
However still need to specify that many states for different environment types.

20. References
1. Dr.Juyang Weng & Dr. Shaouyun Chen “Visual Learning with Navigation as an Example” .Published in IEE September/October 2000.

21. Questions