Pattern Classification
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Pattern Classification All materials in these slides were taken from Pattern Classification (2nd ed) by R. O. Duda, P. E. Hart and D. G. Stork, John Wiley & Sons, 2000 with the permission of the authors and the publisher. Chapter 1: Introduction to Pattern Recognition (Sections 1.1-1.6).

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Pattern ClassificationAll materials in these slides were taken fromPattern Classification (2nd ed)by R. O. Duda, P. E. Hart and D. G. Stork, John Wiley & Sons, 2000with the permission of the authors and the publisher


Chapter 1 introduction to pattern recognition sections 1 1 1 6

Chapter 1: Introduction to Pattern Recognition (Sections 1.1-1.6)

What is Pattern Recognition ?

Machine Perception

An Example

Pattern Recognition Systems

The Design Cycle

Related fields

Learning and Adaptation

Conclusion


What is pattern recognition
What is Pattern Recognition 1.1-1.6)

  • Pattern Recognition is:

    A branch of artificial intelligence concerned with the classification or description of observations.

    Source: The Free On-line Dictionary of Computing (2003-OCT-10)

Pattern Classification, Chapter 1


Machine perception
Machine Perception 1.1-1.6)

  • Build a machine that can recognize patterns:

    • Speech recognition

    • Fingerprint identification

    • OCR (Optical Character Recognition)

    • DNA sequence identification

Pattern Classification, Chapter 1


An example
An Example 1.1-1.6)

  • “Sorting incoming Fish on a conveyor according to species using optical sensing”

    Sea bass

    Species

    Salmon

Pattern Classification, Chapter 1



  • Preprocessing 1.1-1.6)

    • Use a segmentation operation to isolate fishes from one another and from the background

  • Information from a single fish is sent to a feature extractor whose purpose is to reduce the data by measuring certain features

  • The features are passed to a classifier

Pattern Classification, Chapter 1


  • Classification 1.1-1.6)

    • Select the length of the fish as a possible feature for discrimination

Pattern Classification, Chapter 1



The 1.1-1.6)length is a poor feature alone!

Select the lightness as a possible feature.

Pattern Classification, Chapter 1



Pattern Classification, Chapter 1


Lightness

Width

Pattern Classification, Chapter 1



  • We might add other features that are not correlated with the ones we already have. A precaution should be taken not to reduce the performance by adding such “noisy features”

  • Ideally, the best decision boundary should be the one which provides an optimal performance such as in the following figure:

Pattern Classification, Chapter 1


Pattern Classification, Chapter 1 ones we already have. A precaution should be taken not to reduce the performance by adding such “noisy features”


Pattern Classification, Chapter 1


Pattern Classification, Chapter 1 aim of designing a classifier is to correctly classify novel input


Pattern recognition systems
Pattern Recognition Systems aim of designing a classifier is to correctly classify novel input

  • Sensing

    • Use of a transducer (camera or microphone)

    • PR system depends of the bandwidth, the resolution sensitivity distortion of the transducer

Pattern Classification, Chapter 1


Pattern Classification, Chapter 1 aim of designing a classifier is to correctly classify novel input

  • Segmentation and grouping

    • Patterns should be well separated and should not overlap

    • Segmentation faces some problems in

      • Speech recognition(nonsense words).

      • Recognition or grouping together(i,=).

      • Mereology ( i.e. the study of part/whole relationship ).


  • Feature extraction aim of designing a classifier is to correctly classify novel input

    • Discriminative features

    • Invariant features with respect to translation, rotation and scale.

  • Classification

    • Use a feature vector provided by a feature extractor to assign the object to a category

    • The difficulty of classification problem

Pattern Classification, Chapter 1


Pattern Classification, Chapter 1 aim of designing a classifier is to correctly classify novel input

  • Post Processing

    • Uses the output of the classifier to decide on the recommended action.

    • Exploit context input dependent information other than from the target pattern itself to improve performance


Pattern Classification, Chapter 1 aim of designing a classifier is to correctly classify novel input


The design cycle
The Design Cycle aim of designing a classifier is to correctly classify novel input

  • Data collection

  • Feature Choice

  • Model Choice

  • Training

  • Evaluation

  • Computational Complexity

Pattern Classification, Chapter 1


Pattern Classification, Chapter 1 aim of designing a classifier is to correctly classify novel input


  • Data Collection aim of designing a classifier is to correctly classify novel input

    • How do we know when we have collected an adequately large and representative set of examples for training and testing the system?

Pattern Classification, Chapter 1


  • Feature Choice aim of designing a classifier is to correctly classify novel input

    • Depends on the characteristics of the problem domain. Simple to extract, invariant to irrelevant transformation insensitive to noise.

    • Prior knowledge plays major rule in this phase

Pattern Classification, Chapter 1


  • Model Choice aim of designing a classifier is to correctly classify novel input

    • Unsatisfied with the performance of our fish classifier and want to jump to another class of model

Pattern Classification, Chapter 1


  • Training aim of designing a classifier is to correctly classify novel input

    • Use data to determine the classifier. Many different procedures for training classifiers and choosing models

Pattern Classification, Chapter 1


  • Evaluation aim of designing a classifier is to correctly classify novel input

    • Measure the error rate (or performance and switch from one set of features to another one

Pattern Classification, Chapter 1


  • Computational Complexity aim of designing a classifier is to correctly classify novel input

    • What is the trade-off between computational ease and performance?

    • (How an algorithm scales as a function of the number of features, patterns or categories?)

Pattern Classification, Chapter 1


Related fields
Related Fields aim of designing a classifier is to correctly classify novel input

  • Regression : the concept of varying feature linearly with another one(age & length).

  • Interpolation: to classify an object to certain category if it nearly like it.

  • Density estimation: estimating the probability that member of certain category will be found to have a particular feature.

Pattern Classification, Chapter 1


Learning and adaptation
Learning and Adaptation aim of designing a classifier is to correctly classify novel input

  • Supervised learning

    • A teacher provides a category label or cost for each pattern in the training set

  • Unsupervised learning

    • The system forms clusters or “natural groupings” of the input patterns

Pattern Classification, Chapter 1


Conclusion
Conclusion aim of designing a classifier is to correctly classify novel input

  • Reader seems to be overwhelmed by the number, complexity and magnitude of the sub-problems of Pattern Recognition

  • Many of these sub-problems can indeed be solved

  • Many fascinating unsolved problems still remain

Pattern Classification, Chapter 1


References
References aim of designing a classifier is to correctly classify novel input

  • Pattern Classification (2nd ed ) by R. O. Duda, P. E. Hart and D. G. Stork, John Wiley & Sons, 2000

  • http://dictionary.die.net/pattern%20recognition

Pattern Classification, Chapter 1


Questions
Questions! aim of designing a classifier is to correctly classify novel input

Pattern Classification, Chapter 1


Thanks
Thanks aim of designing a classifier is to correctly classify novel input 

Pattern Classification, Chapter 1


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