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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