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Visual Learning with Navigation as an Example

Visual Learning with Navigation as an Example . Dr Juyang Weeng Dr Shaoyun Chen Michigan Sate University. Model Based Methods. PROS Efficient for predictable cases Easier to understand Computationally inexpensive CONS Non generic Not able to deal with every possible case

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Visual Learning with Navigation as an Example

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  1. Visual Learning with Navigation as an Example Dr JuyangWeeng 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.

  3. Example of Model based learning [1]

  4. MODEL FREE METHODS • 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)

  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.

  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 RPTLearning Phase 2 9 1 1 7 6 2 3 3 4 6 7 4 5 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.

  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] [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.

  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

  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

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