Ferns

A Semi-Naive Bayesian Classifier for Fast Patch Classification We show that it is possible to train a Semi-Naive Bayesian Classifier to recognize keypoints without the need for intensive preprocessing stages like fine-scale selection, dominant orientation and affine parameter estimation. We also demonstrate that this approach is superior to previous methods that use weighted averaging of Randomize

[javaholic]

アフィン変換などの前処理なしで高速に画像を認識させる方式

[yasuf]

画像識別・分類に利用