1. A Personal Tour of Machine Learning and Its Applications
Vladimir Y. Mariano, Ph.D. School of Science and Technology
RMIT University

2. Outline Supervised classification Bayesian classification
Nearest neighbors and K-nearest neighbors
Linear discriminant functions
Deep learning (neural networks)
Template matching
Unsupervised classification
Clustering
K-means
Hierarchical
Temporal patterns

3. Supervised versus Unsupervised Learning
The classes are known
Training samples are labelled by their true class labels
Unknown samples are classified into one of the classes
Unsupervised classification
The classes are unknown
The data form natural groupings called clusters
Clustering algorithms find these natural groupings

4. Supervised Classification The classes are known
Supervised Classification The classes are known. Training samples are labelled by their true class labels. Unknown samples are classified into one of the classes.

5. Each “object” has a set of features

6. Shape features for phenotyping 100,000+ rice varieties (Int’l Rice Research Institute)

7. Color spaces assign color features to a pixel Video: papaya.avi

8. Bayesian Classification Given an unknown object X, to what class does it most probably belong? The features are assumed to follow a known distribution, typically multivariate normal distribution. Video: Character recognition (digits16.avi)

9. Nearest neighbor and K nearest neighbors Given an unknown object X, find the training sample that is closest to it. Then classify X as that sample’s class label. A safer way is to find the K samples that are closest to the unknown object. Then do a class voting.

10. Ex. Land-use classification using Landsat multispectral images (5m resolution)?

11. Linear Discriminant Functions We find the best line that divides the two classes, then use this line as the classifier. Video: linear-discrim-cardano.avi

12. Deep Learning These are neural networks with many layers
Deep Learning These are neural networks with many layers Lots of data (where do you get it?) High dimensionality (many, many features)

13. Classifying Coffee Beans (Vinteo Inc.)

14. Template Matching The unknown object is “compared” to a reference object and the degree-of-match is computed. Ex. Correlation-based object detection Video: events-Marquez.avi, sixlanes1b.avi

15. A 5-foot wide conveyor belt that carries coal
Ex. Detecting conveyor belt splices and evaluating their condition (Carnegie Mellon Robotics Institute, 2002)
A 5-foot wide conveyor belt that carries coal

16. Unsupervised Classification The classes are unknown
Unsupervised Classification The classes are unknown. The data form natural groupings called clusters. Clustering algorithms find these natural groupings. In a more general sense, you are looking for “interesting” patterns.

17. Clustering Algorithms “Similar objects flock together in feature space” K-means clustering Adaptive K-means clustering Hierarchical clustering

18. Example: Rice blast (scanned leaf)
Color Analysis for Rice Disease Assessment Int’l Rice Research Institute Uses adaptive K-means clustering in RGB
Example: Rice blast (scanned leaf)

19. Dark areas

20. Normal green areas

21. Light green areas

22. Reddish areas

23. Orange areas

24. Yellow areas

25. What questions can be answered by color segmentation?
What is the area (cm2) of each color region?
What is spatial arrangement of these colors?
Is the distribution of colors normal or abnormal?
How does the color change over time?
Automated leaf color analysis can be used as a tool for detection and identification of diseases, assess nutrient deficiency and nutrient toxicity.

26. Spatio-Temporal Patterns Analysis of retail videos (videomining
Spatio-Temporal Patterns Analysis of retail videos (videomining.com) Video: kkm_raw.avi, kkm_vision.avi Video: person_sizes.avi (research)

27. After 19 years…. Machine Learning is FUN. In the near future…
After 19 years… Machine Learning is FUN. In the near future… Machine Learning will run on most devices.