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Machine Learning. Damon Waring 22 April 2003. Agenda. Problem, Solution, Benefits Machine Learning Overview/Basics Face detection, recognition, and demo How this applies to us Summary. Problem. Software frequently requires users or developers to do simple, repetitive tasks. Solution.

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Machine learning

Machine Learning

Damon Waring

22 April 2003


Agenda
Agenda

  • Problem, Solution, Benefits

  • Machine Learning Overview/Basics

  • Face detection, recognition, and demo

  • How this applies to us

  • Summary


Problem
Problem

Software frequently requires users or developers to do simple, repetitive tasks


Solution
Solution

  • Machine Learning

  • “The study of computer algorithms that improve automatically through experience” –Tom Mitchell, Machine Learning

  • Machine learning uses include:

    • Security (Pattern recognition, face recognition)

    • Business (Stocks, user behaviors)

    • Medical (Research)

    • Ease of Use (Focus of this presentation)

Algorithms that execute based on experience


Benefits
Benefits

  • Makes human-computer interaction easier

  • Relatively simple to integrate

  • Will distinguish your product from others

  • Increase customer satisfaction

  • Will improve simple intelligent systems (ex: Microsoft Word’s grammar checker)

Enhances the user experience


High level operation recognition algorithms

Training Mode

Training Set

Iteratively analyze inputs and refine algorithm

Store learned data

Operation Mode

New input

Process input using learned data

Produce a decision

High Level Operation:Recognition Algorithms

“Learn from nature. It has had 4 billion years to develop its techniques” – My Dad

Recognition algorithms are taught and react like humans


Case study artificial neural network
Case Study: Artificial Neural Network

  • Takes N inputs

  • Calculates the weight each input has on final decision

  • Neuron outputs a 1 if the decision is true, 0 if it is false

  • Groups of neurons make up an artificial neural network

Group of weighted input values determine a binary output


Face detection
Face Detection

  • Image pyramid used to locate faces of different sizes

  • Image lighting compensation

  • Neural Network detects rotation of face candidate

  • Final face candidate de-rotated ready for detection


Face detection con t
Face Detection (Con’t)

  • Submit image to Neural Network

    • Break image into segments

    • Each segment is a unique input to the network

    • Each segment looks for certain patterns (eyes, mouth, etc)

  • Output is likelihood of a face


Face recognition and demo
Face Recognition and demo

  • Demo: Hidden Markov Model Face Recognition

    • Observes location of facial features with respect to each other

    • Person is found through unique “fingerprint” created by distances between features

  • Demo is from OpenCV – Intel’s open source computer vision library

Implementations vary widely and have different success rates


Adobe photoshop album
Adobe Photoshop Album

  • Software that organizes digital pictures

  • Tags are dragged to each photo to categorize it

  • Tagging 100’s of photos is tedious

  • Face recognition could automatically tag photos or replace tags altogether

Machine learning can be used to make everyday apps easier


Current uses of ml
Current Uses of ML

  • DivX – Face detection

  • POV-Ray – Neural Net learns memory accesses

  • Ancestry.com – Uses Optical Character Recognition to digitize newspapers

  • Deep Blue Junior – Less powerful than Deep Blue, but smarter because of Neural Networks


Other areas
Other Areas

  • Artificial Intelligence (AI)

  • Data Mining

  • Fuzzy Logic

  • Optical Character Recognition (OCR)


Summary
Summary

  • Machine learning is possible today

  • Large amounts of research are available

  • Quality open source code available in some areas

  • Will require time and creativity to implement

  • Why do it? Makes human-computer interface simpler


References
References

  • Books

    • Machine Learning by Tom Mitchell (http://www-2.cs.cmu.edu/~tom/mlbook.html)

  • Web sites

    • Hidden Markov Models http://jedlik.phy.bme.hu/~gerjanos/HMM/node2.html

    • Links recommended by PCAI http://www.ics.uci.edu/~mlearn/MLOther.html

    • CMU’s research areas (scroll down): http://www.ri.cmu.edu/people/kanade_takeo.html

    • MIT’s Media Lab: http://www.media.mit.edu/

    • Computer vision links: http://www-2.cs.cmu.edu/afs/cs/project/cil/ftp/html/vision.html

    • Open source computer vision library (OpenCV): http://sourceforge.net/projects/opencvlibrary/

  • Journals

    • PCAI (a great industry magazine, web site is bad)- http://www.pcai.com

    • ScienceDirect (http://www.sciencedirect.com) “Computer Vision and Image Understanding,” “Artificial Intelligence,” “Neural Networks”

    • IEEE Proceedings (http://www.ieee.org) “Pattern Analysis and Machine Intelligence,” “Image Processing”

  • IEEE Papers/Proceedings referenced in this presentation

    • Hidden Markov Models (used in OpenCV Demo) “Maximum likelihood training of the embedded HMM for face detection and recognition.” Nefian, A.V.; Hayes, M.H. III; Image Processing, 2000. Proceedings. 2000 International Conference on, Volume: 1, Pages 33-36.

    • “Neural network-based face detection.” Rowley, H.A.; Baluja, S.; Kanade, T; Pattern Analysis and Machine Intelligence, IEEE Transactions on, Volume 20 Issue 1, Jan 1998. Pages 23-38. (Paper posted at: http://www.ri.cmu.edu/projects/project_271.html)

    • “Rotation Invariant Neural Network-Based Face Detection” http://www.ri.cmu.edu/projects/project_271.html