1 machine learning techniques in image processing by kaan tariman m.s. in computer science csci 8810...
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MACHINE LEARNING TECHNIQUES IN IMAGE PROCESSING
By Kaan TarimanM.S. in Computer Science
CSCI 8810 Course Project
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Outline
Introduction to Machine Learning The example application Machine Learning Methods
Decision Trees Artificial Neural Networks Instant Based Learning
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What is Machine Learning Machine Learning (ML) is constructing
computer programs that develop solutions and improve with experience
Solves problems which can not be solved by enumerative methods or calculus-based techniques
Intuition is to model human way of solving some problems which require experience
When the relationships between all system variables is completely understood ML is not needed
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A Generic System
System… …
1x2x
Nx
1y2y
My1 2, ,..., Kh h h
1 2, ,..., Nx x xx
1 2, ,..., Kh h hh
1 2, ,..., Ky y yy
Input Variables:
Hidden Variables:
Output Variables:
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Learning Task Face recognition
problem: Whose face is this in the picture?
Hard to model describing face and its components
Humans recognize with experience: The more we see the faster we perceive.
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The example Vision module for Sony Aibo Robots that we
have developed for Legged Robot Tournament in RoboCup 2002.
Output of the module is distance and orientation of the target objects: the ball, the players the goals the beacons - used for localization of the robot.
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Aibo’s View
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Main ML Methods Decision Trees Artificial Neural Networks (ANN) Instant-Based Learning Bayesian Methods Reinforcement Learning Inductive Logic Programming (ILP) Genetic Algorithms (GA) Support Vector Machines (SVM)
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Decision Trees
Approximation of discrete functions by a decision tree.
In the nodes of trees are attributes and in the leaves are values of discrete function
Ex: A decision tree for “play tennis”
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Algorithm to derive a tree Until each leaf node is populated by
as homogeneous a sample set as possible: Select a leaf node with an
inhomogeneous sample set. Replace that leaf node by a test node
that divides the inhomogeneous sample set into minimally inhomogeneous subsets, according to an entropy calculation.
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Color Classification
Data set includes pixel values labeled with different colors manually
The tree classifies a pixel to a color according to its Y,U,V values.
Adaptable for different conditions.
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How do we construct the data set?1) Open an image taken by the robot
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How do we construct the data set?2) Label the pixels with colors[Y,U,V,color] entries are created for each pixel labeled
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How do we construct the data set?3) Use the ML method and display results
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The decision tree output
The data set is divided into training and validation set
After training the tree is evaluated with validation set.
Training should be done carefully, avoiding bias.
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Artificial Neural Networks (ANN) Made up of interconnected processing
elements which respond in parallel to a set of input signals given to each
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ANN Algorithm
Training algorithm adjusts the weights reducing the error between the known output values and the actual values
At first, the outputs are arbitrary. As cases are reintroduced repeatedly
ANN gives more right answers. Test set is used to stop training. ANN is validated with unseen data
(validation set)
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ANN output for our example
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Face Recognition with ANN
Problem: Orientation of face Input nodes are pixel values
of the image. (32 x 32) Output has 4 nodes (right,
left, up, straight) 6 hidden nodes
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Face Recognition with ANN Hidden nodes normally does not infer
anything, in this case we can observe some behavior.
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Instance Based Learning A learn-by-memorizing method: K-Nearest
Neighbor Given a data set {Xi, Yi} it estimates values
of Y for X's other than those in the sample. The process is to choose the k values of Xi
nearest the X and average their Y values. Here k is a parameter to the estimator. The
average could be weighted, e.g. with the closest neighbor having the most impact on the estimate.
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KNN facts Database of
knowledge about known instances is required – memory complexity
“Lazy learning”, no model for the hypothesis
Ex: Color classification A voting method is
applied in order to output a color class for the pixel.
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Summary Machine Learning is an interdisciplinary
field involving programs that improve by experience
ML is good for pattern recognition, object extraction and color classification etc. problems in image processing problem domain.
3 methods are considered: Decision Trees Artificial Neural Networks Instant Based Learning
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Thank you!
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