Training for Task Specific Keypoint Detection

In this paper, we show that a better performance can be achieved by training a keypoint detector to only find those points that are suitable to the needs of the given task. We demonstrate our approach in an urban environment, where the keypoint detector should focus on stable man-made structures and ignore objects that undergo natural changes such as vegetation and clouds. We use WaldBoost learning with task specific training samples in order to train a keypoint detector with this capability. We show that our aproach generalizes to a broad class of problems where the task is known beforehand.

[1]  Bernhard Schölkopf,et al.  Learning an Interest Operator from Human Eye Movements , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[2]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[3]  Jean-Nicolas Ouellet,et al.  ASN: Image Keypoint Detection from Adaptive Shape Neighborhood , 2008, ECCV.

[4]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[5]  Cordelia Schmid,et al.  A Comparison of Affine Region Detectors , 2005, International Journal of Computer Vision.

[6]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

[7]  Jiri Matas,et al.  WaldBoost - learning for time constrained sequential detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[8]  Tom Drummond,et al.  Faster and Better: A Machine Learning Approach to Corner Detection , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Jiri Matas,et al.  Learning a Fast Emulator of a Binary Decision Process , 2007, ACCV.

[10]  Tom Drummond,et al.  Machine Learning for High-Speed Corner Detection , 2006, ECCV.

[11]  A. L. Yarbus.  Eye movements and vision Plenum Press , 1967 .

[12]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

[13]  Maarten Vergauwen,et al.  Web-based 3D Reconstruction Service , 2006, Machine Vision and Applications.

[14]  Kurt Konolige,et al.  CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching , 2008, ECCV.

[15]  Steven M. Seitz,et al.  Photo tourism: exploring photo collections in 3D , 2006, ACM Trans. Graph..

[16]  Andrew J. Davison,et al.  Active Matching , 2008, ECCV.

[17]  Yoram Singer,et al.  Improved Boosting Algorithms Using Confidence-rated Predictions , 1998, COLT' 98.

[18]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[19]  A. L. I︠A︡rbus.  Eye Movements and Vision , 1967 .

[20]  Axel Pinz,et al.  Computer Vision – ECCV 2006 , 2006, Lecture Notes in Computer Science.