Visual Learning with Navigation as an Example

Dr Juyang WeengDr Shaoyun Chen

Michigan Sate University

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.

Model Based Methods

Example of Model based learning

[1]

Automatically learn the model

Xt input image in rc dimensional space(S)Yt+1 control signal in space CThe image needs to be vectorized.

GOAL :Approximate the function f

MODEL FREE METHODS

Yt+1=f(Xt)

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.

Recursion Partition Tree

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.

Learning Phase

How to construct the RPTLearning Phase

6 7

12 3

4 5

8 9

12

3

45

6 7

8

9

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.

Performance phase

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.

Automatic Feature Derivation

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.

PCA

PCA v/s LDA

[1]

PCA 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.

LDA

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)

Using States

The Observation driven Markov Model[1]

[1]

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.

Dealing with local attention

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

Incremental Learning

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.

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.

Shoslif versus other methods

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.

Conclusion

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

References

Questions