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Contextual performance prediction for low-level image analysis algorithms.
B Chalmond1, C Graffigne, M Prenat
1CMLA, Ecole Normale Superieure, 94235 Cachan Cedex, France. Bernard.Chalmond@cmla.ens-cachan.fr
Summary
This study introduces a method to predict algorithm accuracy without running the algorithm, using local image context. This helps determine if an algorithm is suitable for specific image properties, improving image analysis.
Area of Science:
- Computer Vision
- Image Processing
- Machine Learning
Background:
- Evaluating algorithm performance often requires running the algorithm, which can be computationally expensive.
- Understanding algorithm suitability for specific image types (e.g., varying contrast, noise, resolution) is crucial for effective image analysis.
Purpose of the Study:
- To develop a generic approach for predicting algorithm output accuracy based on local image context.
- To enable the qualification of algorithm appropriateness for images with diverse properties without executing the algorithm.
Main Methods:
- Utilized a logistic regression model for prediction, trained on contextual variables.
- Defined context (c) using Gabor components, entropy, and signal-to-noise ratio.
- Applied the prediction technique to aerial infrared images for edge detection and displacement estimation algorithms.
Main Results:
- Demonstrated the feasibility of predicting algorithm performance from contextual features.
- Showcased the model's ability to generalize across different image properties and algorithms.
- Validated the approach on real-world aerial infrared imagery.
Conclusions:
- The proposed generic approach offers a computationally efficient way to assess algorithm suitability.
- Context-based performance prediction can guide algorithm selection for specific image analysis tasks.
- This method enhances the practical application of image processing algorithms by ensuring appropriate usage.