Review — Comparative Study of Classifiers for Blurred Images (Blur Classification)



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Review — Comparative Study of Classifiers for Blurred Images (Blur Classification)

Feature Extraction Using DCT, Classification Using RF Outperforms Naïve Bayes, MLP, k-NN, SVM

In this story, Comparative Study of Classifiers for Blurred Images, (Gueraichi SAI’20), by Houari Boumediene University of Science and Technology, is reviewed. In this paper:

  • A robust learning model based on the transformation into DCT is built, for the classification of images according to their degree of blur.
  • Naïve Bayes, MLP, k-NN, SVM, RF are tried. Finally, Random Forest (RF) is found to be the best.

This is a paper in 2020 SAI. (Sik-Ho Tsang @ Medium)

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