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Pattern recognition in image processing using interpixel correlation.

S L Sclove1

  • 1Department of Mathematics, University of Illinois at Chicago Circle, Chicago, IL 60680.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 27, 2011
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This study introduces a two-dimensional Markov model to improve image classification by incorporating within-object interpixel correlation. This method enhances accuracy when correlation varies significantly between objects.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Image Processing

Background:

  • Image classification often relies on pixel intensity mean and variance.
  • Interpixel correlation within objects can provide additional discriminative information.
  • Variability in interpixel correlation across different objects poses a challenge for traditional classifiers.

Purpose of the Study:

  • To develop a classification scheme that effectively utilizes within-object interpixel correlation.
  • To address the limitations of classifiers that ignore interpixel correlation.
  • To improve classification accuracy in scenarios with varying interpixel correlations.

Main Methods:

  • A two-dimensional Markov model was employed to represent and incorporate interpixel correlation.
  • The model was integrated into a classification framework alongside pixel intensity mean and variance.
  • The approach was evaluated on its ability to leverage correlation information.

Main Results:

  • The proposed method demonstrated the utility of interpixel correlation in classification tasks.
  • Classification performance was enhanced by considering the varying interpixel correlations.
  • The two-dimensional Markov model provided an effective way to capture spatial dependencies.

Conclusions:

  • Incorporating within-object interpixel correlation using a two-dimensional Markov model is beneficial for image classification.
  • This approach offers improved robustness and accuracy, especially when interpixel correlations differ between objects.
  • The study highlights the importance of considering higher-order statistical properties in pixel data.