1. Linear Classifiers Rubine & CA-Linear Ruben Balcazar

2. Classification Task Given a group of labels, can we identify an input as belonging to one of those labels?

3. To be more concrete LABELS Cat Dog INPUT

4. To be more concrete LABELS Cat Dog INPUT How could we make a machine that could do these classifications?

5. Features Qualities that can describe and differentiate our labels: Size Weight Fluffiness Appetite Time spent sleeping

6. One Feature Size

7. One Feature Size Training Set

8. One Feature Size Input

9. One Feature Size Input

10. Two Feature Size Appetite

11. Two Feature Size Appetite

12. Two Feature Size Appetite Good Linear Classifier

13. Two Feature Size Appetite Bad Linear Classifier

14. Features Vector Allows us to represent any number of n features:

15. Weights Not every feature is equally important! Every feature f has an associated weight w to reflect how much it contributes to classification. Weights are found through the use of training sets.

16. Evaluation Score In two class(label) systems, score works as a threshold: In more complex systems, the class with the highest score is the best match. In gesture recognition, our classes are the different gesture types.

17. Rubine Feature are extracted from gesture point data: Example: Feature 1 is the cosine of the initial angle of the gesture. Feature 4 is the angle of the diagonal of the gesture’s bounding box. Rubine specifies 13 features total.

18. Rubine

19. Finding the Weights

21. CCOV is also an FxF matrix

22. Finding the Weights

25. Rejection LABELS Cat Dog INPUT Linear Classifiers ALWAYS return a class.

26. Rejection LABELS Cat Dog INPUT What if our input is not relevant?

27. Rejection LABELS Cat Dog INPUT Ruben is a dog?

28. Rejection LABELS Cat Dog INPUT Ambiguous or irrelevant classifications should be rejected.

29. Rejection in Rubine

30. CA-Linear CA-Linear is a modification of Rubine. CA stands for “Context Aware.” CA-Linear uses “context” to remove potential gesture candidates from a classification.

31. Context : First Modification Rubine: CA-Linear: Context is denoted by I, an indicator or relative context function.

32. ParKorror’s Context Player Context: Environmental context:

33. Gesture Prior Function: Second Modification In ParKorror:

34. Gesture Prior Function: Second Modification

35. Summary: Rubine: Gesture is defined by a features vector The supported gestures use examples(training sets) to calculate the weight of these features using CCOV matrixes Input gestures are classified by evaluation equation The highest evaluation score is the classified gesture We reject classification if P(g|c) < 0.95.

36. Summary

37. Question s?