Session 1: Gesture Recognition & Machine Learning Fundamentals

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1 IAP Gesture Recognition Workshop Session 1: Gesture Recognition & Machine Learning Fundamentals Nicholas Gillian Responsive Environments, MIT Media Lab Tuesday 8th January, 2013

2 My Research

3 My Research Gesture Recognition for Musician Computer Interaction

4 My Research Gesture Recognition for Musician Computer Interaction Rapid Learning

5 My Research Gesture Recognition for Musician Computer Interaction Rapid Learning Free-air Gestures & Fine-grain Control

6 My Research Gesture Recognition for Musician Computer Interaction Rapid Learning Free-air Gestures & Fine-grain Control Creating tools and software that enable a more diverse group of individuals to integrate gesture-recognition into their own interfaces, art installations, and musical instruments

7 My Research Gesture Recognition for Musician Computer Interaction Rapid Learning Free-air Gestures & Fine-grain Control Creating tools and software that enable a more diverse group of individuals to integrate gesture-recognition into their own interfaces, art installations, and musical instruments EyesWeb Gesture Recognition Toolkit

8 Schedule Machine Learning 101 Hello World Gesture Recognition Installation & Setup Introduction to the Gesture Recognition Toolkit Lunch Hands-on Coding Sessions

9 Basic Pattern Recognition Problem

10 Basic Pattern Recognition Problem

11 Basic Pattern Recognition Problem Might work for simple cases...

12 Basic Pattern Recognition Problem Can be more difficult with multidimensional data!

13 Basic Pattern Recognition Problem Event B Can be more difficult with multiple events!

14 Machine Learning Machine Learning 101

15 Machine Learning Dataset

16 Machine Learning ML can automatically infer the underlying behavior/rules of this data

17 Machine Learning These rules can then be used to make predictions about future data

18 Machine Learning

19 Machine Learning The three main phases of machine learning: Data Collection Learning Prediction

20 Machine Learning Machine Learning is commonly used to solve two main problems:

21 Machine Learning Machine Learning is commonly used to solve two main problems: CLASSIFICATION

22 Machine Learning Machine Learning is commonly used to solve two main problems: CLASSIFICATION

23 Machine Learning Machine Learning is commonly used to solve two main problems: CLASSIFICATION

24 Machine Learning Machine Learning is commonly used to solve two main problems: CLASSIFICATION

25 Machine Learning Machine Learning is commonly used to solve two main problems: CLASSIFICATION REGRESSION

26 Machine Learning Machine Learning is commonly used to solve two main problems: CLASSIFICATION REGRESSION

27 Machine Learning Machine Learning is commonly used to solve two main problems: CLASSIFICATION Discrete Output, representing the most likely class that the input x belongs to REGRESSION

28 Machine Learning Machine Learning is commonly used to solve two main problems: CLASSIFICATION Discrete Output, representing the most likely class that the input x belongs to REGRESSION Continuous Output, mapping the N dimensional input vector x to an M dimensional vector y

29 Machine Learning Main types of learning:

30 Machine Learning Main types of learning: SUPERVISED LEARNING

31 Machine Learning Main types of learning: Class A Class B SUPERVISED LEARNING

32 Machine Learning Main types of learning: Class A Class B SUPERVISED LEARNING UNSUPERVISED LEARNING

33 Machine Learning Main types of learning: Class A Class B SUPERVISED LEARNING UNSUPERVISED LEARNING

34 Machine Learning Main types of learning: Class A Class B SUPERVISED LEARNING UNSUPERVISED LEARNING

35 Machine Learning Main types of learning: Class A Class B SUPERVISED LEARNING UNSUPERVISED LEARNING many others, such as semi-supervised learning, reinforcement learning, active learning, deep learning, etc..

36 Machine Learning Main types of learning: Class A Class B SUPERVISED LEARNING UNSUPERVISED LEARNING many others, such as semi-supervised learning, reinforcement learning, active learning, deep learning, etc..

37 Machine Learning Supervised Learning

38 Machine Learning Training Data

39 Machine Learning Training Data

40 Machine Learning Training Data Input Vector

41 Machine Learning Training Data Input Vector Target Vector

42 Machine Learning Training Data Learning Algorithm Input Vector Target Vector

43 Machine Learning Model Training Data Learning Algorithm Input Vector Target Vector

44 Machine Learning Model Training Data Learning Algorithm New Datum Prediction

45 Machine Learning Model Training Data Learning Algorithm Class A New Datum Predicted Class Prediction

46 Machine Learning Model Training Data Learning Algorithm Class A New Datum Predicted Class Prediction

47 Machine Learning Offline Model Training Data Learning Algorithm Class A New Datum Predicted Class Prediction

48 Machine Learning Model Training Data Learning Algorithm Online Class A New Datum Predicted Class Prediction

49 The Learning Process Model Training Data Learning Algorithm Class A New Datum Predicted Class Prediction

50 The Learning Process

51 The Learning Process

52 The Learning Process DECISION BOUNDARY

53 The Learning Process DECISION BOUNDARY

54 The Learning Process DECISION BOUNDARY

55 The Learning Process There are many possible decision boundaries! How do we choose the best one?

56 The Learning Process Minimize some error: Num Correctly Classified Examples Num Examples

57 The Learning Process Minimize some error: Num Correctly Classified Examples Num Examples Error = 0.31

58 The Learning Process Minimize some error: Num Correctly Classified Examples Num Examples Error = 0.22

59 The Learning Process Minimize some error: Num Correctly Classified Examples Num Examples Error = 0.12

60 The Learning Process Stop when this error is small Error = 0

61 The Learning Process Need to be careful that we don t overtrain the model...

62 The Learning Process Need to be careful that we don t overtrain the model... Complex decision boundary gets a perfect result on the training data

63 The Learning Process Need to be careful that we don t overtrain the model... Complex decision boundary gets a perfect result on the training data But it might fail terribly with new data

64 The Learning Process Need to be careful that we don t overtrain the model... Complex decision boundary gets a perfect result on the training data But it might fail terribly with new data This is know as OVERFITTING

65 The Learning Process Need to be careful that we don t overtrain the model... A very simple decision boundary might not work either

66 The Learning Process Need to be careful that we don t overtrain the model... A very simple decision boundary might not work either This is know as UNDERFITTING

67 The Learning Process Need to be careful that we don t overtrain the model... Instead, a less complex decision boundary might work much better, even if it does not perfectly reduce the error on the training data

68 The Learning Process Need to be careful that we don t overtrain the model... Instead, a less complex decision boundary might work much better, even if it does not perfectly reduce the error on the training data A model s ability to correctly predict the values of unseen data is know as GENERALIZATION

69 Testing a Model s Generalization Ability

70 Testing a Model s Generalization Ability Important not to use the training data to test a model!

71 Testing a Model s Generalization Ability Important not to use the training data to test a model! Instead use a test dataset

72 Testing a Model s Generalization Ability Important not to use the training data to test a model! Instead use a test dataset Dataset

73 Testing a Model s Generalization Ability Important not to use the training data to test a model! Instead use a test dataset Random Spilt Dataset

74 Testing a Model s Generalization Ability Important not to use the training data to test a model! Instead use a test dataset Random Spilt Training Dataset Dataset

75 Testing a Model s Generalization Ability Important not to use the training data to test a model! Instead use a test dataset Random Spilt Training Dataset Dataset Test Dataset

76 Testing a Model s Generalization Ability Sometimes there is not enough data to create a test dataset

77 Testing a Model s Generalization Ability Sometimes there is not enough data to create a test dataset Instead use K-FOLD CROSS VALIDATION

78 Testing a Model s Generalization Ability Sometimes there is not enough data to create a test dataset Instead use K-FOLD CROSS VALIDATION Dataset

79 Testing a Model s Generalization Ability Sometimes there is not enough data to create a test dataset Instead use K-FOLD CROSS VALIDATION Random Partition Dataset Partition Data into K Folds

80 Testing a Model s Generalization Ability Sometimes there is not enough data to create a test dataset Instead use K-FOLD CROSS VALIDATION Training Dataset Random Partition Dataset Fold 1

81 Testing a Model s Generalization Ability Sometimes there is not enough data to create a test dataset Instead use K-FOLD CROSS VALIDATION Training Dataset Random Partition Dataset Test Dataset Fold 1

82 Testing a Model s Generalization Ability Sometimes there is not enough data to create a test dataset Instead use K-FOLD CROSS VALIDATION Test Dataset Random Partition Training Dataset Dataset Fold 2

83 Testing a Model s Generalization Ability Sometimes there is not enough data to create a test dataset Instead use K-FOLD CROSS VALIDATION Test Dataset Random Partition Training Dataset Dataset Fold 3

84 Testing a Model s Generalization Ability Classification Accuracy = Num Correctly Classified Examples Num Test Examples

85 Testing a Model s Generalization Ability Classification Accuracy = Num Correctly Classified Examples Num Test Examples Precision k = Num Correctly Classified Examples for Class k Num Examples Classified as Class k

86 Testing a Model s Generalization Ability Classification Accuracy = Num Correctly Classified Examples Num Test Examples Precision k = Num Correctly Classified Examples for Class k Num Examples Classified as Class k Recall k = Num Correctly Classified Examples for Class k Num Class k Examples

87 Testing a Model s Generalization Ability Classification Accuracy = Num Correctly Classified Examples Num Test Examples Precision k = Num Correctly Classified Examples for Class k Num Examples Classified as Class k Recall k = Num Correctly Classified Examples for Class k Num Class k Examples F-measure k = 2 * Precision k * Recall k Precision k + Recall k

88 Testing a Model s Generalization Ability Classification Task: Detect the coffee mugs in the image

89 Testing a Model s Generalization Ability Segmentation algorithm gives us 13 possible candidates

90 Testing a Model s Generalization Ability The classification algorithm predicts that the following 5 objects are coffee mugs

91 Testing a Model s Generalization Ability Accuracy =? The classification algorithm predicts that the following 5 objects are coffee mugs

92 Testing a Model s Generalization Ability Accuracy = 10/13 = items were classified correctly, 3 were not

93 Testing a Model s Generalization Ability Accuracy = 10/13 = 0.78 Precision =? Precision k = Num Correctly Classified Examples as Class k Num Examples Classified as Class k

94 Testing a Model s Generalization Ability Accuracy = 10/13 = 0.78 Precision = 4/5 = 0.8 Precision k = Num Correctly Classified Examples as Class k Num Examples Classified as Class k

95 Testing a Model s Generalization Ability Accuracy = 10/13 = 0.78 Precision = 4/5 = 0.8 Recall =? Recall k = Num Correctly Classified Examples as Class k Num Class k Examples

96 Testing a Model s Generalization Ability Accuracy = 10/13 = 0.78 Precision = 4/5 = 0.8 Recall = 4/6 = 0.6 Recall k = Num Correctly Classified Examples as Class k Num Class k Examples

97 Testing a Model s Generalization Ability Accuracy = 10/13 = 0.78 Precision = 4/5 = 0.8 Recall = 4/6 = 0.6 F-Measure = 2 * 0.8 * = 0.69 Recall k = Num Correctly Classified Examples as Class k Num Class k Examples

98 A Simple Classifier Example

99 K-Nearest Neighbor Classifier (KNN)

100 K-Nearest Neighbor Classifier (KNN) Training Data Training Data: - M Labelled Training Examples - Each example is an N- Dimensional Vector

101 K-Nearest Neighbor Classifier (KNN) Training Data Learning Algorithm Training Phase: - Simply save the labelled training examples

102 K-Nearest Neighbor Classifier (KNN) Training Data Learning Algorithm Model Model: - Labelled training examples

103 K-Nearest Neighbor Classifier (KNN) Class? Training Data Learning Algorithm Model New Datum Predicted Class Prediction Phase: - Given a new N-Dimensional Vector, predict which class it belongs to

104 K-Nearest Neighbor Classifier (KNN) Class? Training Data Learning Algorithm Model New Datum Predicted Class Prediction Phase: - Given a new N-Dimensional Vector, predict which class it belongs to - Find the K Nearest Neighbors in the training examples - Classify x as the most likely class (i.e. the most common class in the K Nearest Neighbors) Class A: 2 Class B: 1 Likelihood of belonging to Class A = 0.6

105 K-Nearest Neighbor Classifier (KNN) Class? Training Data Learning Algorithm Model New Datum Predicted Class Prediction Phase: - Given a new N-Dimensional Vector, predict which class it belongs to - Find the K Nearest Neighbors in the training examples - Classify x as the most likely class (i.e. the most common class in the K Nearest Neighbors) Class A: 4 Class B: 6 Likelihood of belonging to Class B = 0.6

106 Hello World - KNN Demo

107 Gesture Recognition

108 Gesture Recognition

109 Gesture Recognition Instead of using the raw data as input to the learning algorithm, we might want to pre-process the data (i.e. scale it, smooth it) and also compute some features from the data which make the classification task easier for the machine-learning algorithm

110 Gesture Recognition Important that we also use the same pre-processing and feature extraction methods when predicting the new data!

111 Gesture Recognition Important that we also use the same pre-processing and feature extraction methods when predicting the new data!

112 Gesture Recognition Classification Task: Recognize different postures of a dancer

113 Gesture Recognition Classification Task: Recognize different postures of a dancer Input Vector: * 480 * 3 =

114 Gesture Recognition Preprocessing: Background Subtraction

115 Gesture Recognition Preprocessing: Background Subtraction Input Vector: * 480 =

116 Gesture Recognition Feature Extraction: Bounding Box Input Vector: = 2

117 Gesture Recognition Important that we also use the same pre-processing and feature extraction methods when predicting the new data!

118 Gesture Recognition As well as pre-processing the input to the classification algorithm, we might also want to process the output of the classifier

119 Gesture Recognition Choosing the right features is REALLY IMPORTANT!

120 Gesture Recognition Choosing the right features is REALLY IMPORTANT! Choosing the right ML algorithm is also REALLY IMPORTANT!

121 Gesture Recognition Choosing the right algorithm to solve your problem:

122 Gesture Recognition Choosing the right algorithm to solve your problem: First you need to categorize your problem:

123 Gesture Recognition Choosing the right algorithm to solve your problem: First you need to categorize your problem: Problem Discrete or Continuous Output?

124 Gesture Recognition Choosing the right algorithm to solve your problem: First you need to categorize your problem: Problem Discrete or Continuous Output? Continuous

125 Gesture Recognition Choosing the right algorithm to solve your problem: First you need to categorize your problem: Problem Discrete or Continuous Output? Continuous REGRESSION PROBLEM

126 Gesture Recognition Choosing the right algorithm to solve your problem: First you need to categorize your problem: Discrete Static Posture or Temporal Gesture? Problem Discrete or Continuous Output? Continuous REGRESSION PROBLEM

127 Gesture Recognition Choosing the right algorithm to solve your problem: First you need to categorize your problem: Discrete Static Posture or Temporal Gesture? STATIC CLASSIFICATION PROBLEM Problem Discrete or Continuous Output? Continuous REGRESSION PROBLEM

128 Gesture Recognition Choosing the right algorithm to solve your problem: First you need to categorize your problem: Problem Discrete Discrete or Continuous Output? Static Posture or Temporal Gesture? STATIC CLASSIFICATION PROBLEM TEMPORAL CLASSIFICATION PROBLEM Continuous REGRESSION PROBLEM

129 Gesture Recognition Choosing the right algorithm to solve your problem: STATIC CLASSIFICATION PROBLEM TEMPORAL CLASSIFICATION PROBLEM PROBLEM

130 Gesture Recognition Choosing the right algorithm to solve your problem: STATIC CLASSIFICATION PROBLEM Adaptive Naive Bayes Classifier (ANBC) TEMPORAL CLASSIFICATION PROBLEM PROBLEM

131 Gesture Recognition Choosing the right algorithm to solve your problem: STATIC CLASSIFICATION PROBLEM TEMPORAL CLASSIFICATION PROBLEM Adaptive Naive Bayes Classifier (ANBC) K-Nearest Neighbor (KNN) PROBLEM

132 Gesture Recognition Choosing the right algorithm to solve your problem: STATIC CLASSIFICATION PROBLEM TEMPORAL CLASSIFICATION PROBLEM Adaptive Naive Bayes Classifier (ANBC) K-Nearest Neighbor (KNN) AdaBoost PROBLEM

133 Gesture Recognition Choosing the right algorithm to solve your problem: STATIC CLASSIFICATION PROBLEM TEMPORAL CLASSIFICATION PROBLEM Adaptive Naive Bayes Classifier (ANBC) K-Nearest Neighbor (KNN) AdaBoost Decision Trees PROBLEM

134 Gesture Recognition Choosing the right algorithm to solve your problem: STATIC CLASSIFICATION PROBLEM TEMPORAL CLASSIFICATION PROBLEM Adaptive Naive Bayes Classifier (ANBC) K-Nearest Neighbor (KNN) AdaBoost Decision Trees Support Vector Machine (SVM) PROBLEM

135 Gesture Recognition Choosing the right algorithm to solve your problem: STATIC CLASSIFICATION PROBLEM TEMPORAL CLASSIFICATION PROBLEM PROBLEM

136 Gesture Recognition Choosing the right algorithm to solve your problem: STATIC CLASSIFICATION PROBLEM TEMPORAL CLASSIFICATION PROBLEM Hidden Markov Model (HMM) PROBLEM

137 Gesture Recognition Choosing the right algorithm to solve your problem: STATIC CLASSIFICATION PROBLEM TEMPORAL CLASSIFICATION PROBLEM Hidden Markov Model (HMM) Dynamic Time Warping (DTW) PROBLEM

138 Gesture Recognition Choosing the right algorithm to solve your problem: STATIC CLASSIFICATION PROBLEM TEMPORAL CLASSIFICATION PROBLEM PROBLEM

139 Gesture Recognition Choosing the right algorithm to solve your problem: Static Posture or Temporal Gesture? STATIC CLASSIFICATION PROBLEM TEMPORAL CLASSIFICATION PROBLEM REGRESSION PROBLEM

140 Gesture Recognition Choosing the right algorithm to solve your problem: Static Posture or Temporal Gesture? STATIC CLASSIFICATION PROBLEM TEMPORAL CLASSIFICATION PROBLEM REGRESSION PROBLEM Artificial Neural Network (ANN)

141 Gesture Recognition Choosing the right algorithm to solve your problem: Static Posture or Temporal Gesture? STATIC CLASSIFICATION PROBLEM TEMPORAL CLASSIFICATION PROBLEM REGRESSION PROBLEM

142 Gesture Recognition Choosing the right algorithm to solve your problem:

143 Machine Learning Resources - Great books to get started: Marsland (2009): Machine Learning: An Algorithmic Perspective Witten (2011): Data Mining: Practical Machine Learning Tools and Techniques - More detailed books: Bishop (2007): Pattern Recognition and Machine Learning - Online Lectures: Duda (2001): Pattern Classification Prof. Andrew Ng (Stanford University), Machine Learning Lectures (search for Machine Learning (Stanford) in youtube)

144 Gesture Recognition Toolkit

145 Gesture Recognition Toolkit Adaptive Naive Bayes Classifier K-Nearest Neighbor Dynamic Time Warping Support Vector Machine Classification Modules Regression Modules Artificial Neural Networks Gaussian Mixture Model

146 Gesture Recognition Toolkit Adaptive Naive Bayes Classifier K-Nearest Neighbor Dynamic Time Warping Support Vector Machine Classification Modules Regression Modules Artificial Neural Networks Gaussian Mixture Model Circular Buffer Data Linear Algebra Utils Structures Matrix Timer Training Data Structures Random Range Tracker

147 Gesture Recognition Toolkit Filters FFT Pre Processing Modules Adaptive Naive Bayes Classifier Derivative Zero Crossing Feature Extraction Modules Peak Detection Zero Crossing Counter Movement Trajectory Features Post Processing Modules K-Nearest Neighbor Dynamic Time Warping Support Vector Machine Classification Modules Regression Modules Class Label Filters Artificial Neural Networks Gaussian Mixture Model Circular Buffer Data Linear Algebra Utils Structures Matrix Timer Training Data Structures Random Range Tracker

148 Gesture Recognition Toolkit Classification Modules Pre Processing Modules Feature Extraction Modules Regression Post Processing Modules Modules

149 Gesture Recognition Toolkit Classification Modules Pre Processing Modules Feature Extraction Modules Regression Post Processing Modules Modules Gesture Recognition Pipeline

150 Gesture Recognition Toolkit This is how you setup a new pipeline and set the classifier

151 Gesture Recognition Toolkit This is how you would change the classifier

152 Gesture Recognition Toolkit This is how you setup a more complex pipeline

153 Gesture Recognition Toolkit This is how you train the algorithm at the core of the pipeline

154 Gesture Recognition Toolkit This is how you test the accuracy of the pipeline

155 Gesture Recognition Toolkit You can then easily access the accuracy, precision, recall, etc.

156 Gesture Recognition Toolkit If you want to run k-fold cross validation, then simply state the k-value when you call the train method and the pipeline will do the rest

157 Gesture Recognition Toolkit This is how you perform real-time classification

158 Gesture Recognition Toolkit After the prediction you can then get the predicted class label, predication likelihoods, etc.

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