Fast Keypoint Recognition Using Random Ferns

While feature point recognition is a key component of modern approaches to object detection, existing approaches require computationally expensive patch preprocessing to handle perspective distortion. In this paper, we show that formulating the problem in a Naive Bayesian classification framework makes such preprocessing unnecessary and produces an algorithm that is simple, efficient, and robust. Furthermore, it scales well as number of classes grows. To recognize the patches surrounding keypoints, our classifier uses hundreds of simple binary features and models class posterior probabilities. We make the problem computationally tractable by assuming independence between arbitrary sets of features. Even though this is not strictly true, we demonstrate that our classifier nevertheless performs remarkably well on image datasets containing very significant perspective changes.

• Type: Journal
• Year: 2010
• Where: PAMI
• Authors: Mustafa Özuysal, Michael Calonder, Vincent Lepetit, Pascal Fua
• Keywords: Computationally Expensive, Naive Bayesian Classification, PAMI 2010, Simple Binary Features