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Error Detection and Classification in Machine Learning for Computer Vision

This article discusses the design and validation of classifiers for detecting and classifying errors in computer vision using machine learning. It explores the decision boundaries in complex data patterns and provides insights into the design of a classifier for specific applications. The article also covers the estimation of errors in a learning system and the importance of training and testing samples for accurate error estimation.

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Error Detection and Classification in Machine Learning for Computer Vision

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  1. i=2I

  2. i= i=

  3. i=arbitrary

  4. Complex decision boundaries

  5. Machine LearningDesign & Validation of Classifiers

  6. Computer Vision Detection of Errors A/D Converter Sensor Object

  7. + + + + + o + + + + o + o o o o o o o o Pattern of Data X1 X2

  8. Samples Learning System Classifiers Learning System

  9. Classification Systems Data for classification Decision Pertaining to class Classifier

  10. Samples for training Classifier for Specific application Case Variables (Features) Classes Learning System Classes Pertaining to samples Values of variables (xi) Classifier type Design of a classifier

  11. Class (+) Class (-) Classification (+) Correct (+/+) Error (-/+) Classification (-) Error (+/-) Correct (-/-) Estimating the execution of a learning system What is an error? Reason for error (estimate) = number of errors number of cases

  12. Apparent and true error Samples for training New cases Classifier Apparent reason for error True error

  13. Samples Error estimationusing samples for training and samples for testing Cases for training the classifier Cases for testing the classifier

  14. Example: 1-d Class 1: n1 = 5 X1 Train Y1 Train Class 2: n1 = 5 X2 Train Y2 Test

  15. Estimation of Parameters

  16. Classification ML Rule Class 1

  17. Classification ML Rule Class 2

  18. A simpler Classification ML Rule Class 1

  19. Classification ML Rule Class 2

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