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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!