Abstract
This paper generalizes the technique described in [1] to gray-scale image processing applications. This method chooses a subset of variables W (i.e. pixels seen through a window) that maximizes the information observed in a set of training data by mean conditional entropy minimization. The task is formalized as a combinatorial optimization problem, where the search space is the powerset of the candidate variables and the measure to be minimized is the mean entropy of the estimated conditional probabilities. As a full exploration of the search space requires an enormous computational effort, some heuristics of the feature selection literature are applied. The introduced approach is mathematically sound and experimental results with texture recognition application show that it is also adequate to treat problems with gray-scale images.
Keywords
- Feature Vector
- Feature Selection
- Mutual Information
- Conditional Entropy
- Feature Selection Algorithm
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
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Martins, D.C., Cesar, R.M., Barrera, J. (2005). Automatic Window Design for Gray-Scale Image Processing Based on Entropy Minimization. In: Sanfeliu, A., Cortés, M.L. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2005. Lecture Notes in Computer Science, vol 3773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11578079_85
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DOI: https://doi.org/10.1007/11578079_85
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29850-2
Online ISBN: 978-3-540-32242-9
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