A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Statistical Learning of Multi-view Face Detection
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Robust Real-Time Face Detection
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Joint Haar-like Features for Face Detection
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Fast Human Detection Using a Cascade of Histograms of Oriented Gradients
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
High-Performance Rotation Invariant Multiview Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative Feature Co-Occurrence Selection for Object Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
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For the purpose of object detection, Haar-Like Features (HLF) proposed by Viola [13][14] are very famous. To classify images, usually HLF and its extensions used only image intensity. However, it is well known that the gradient information of image intensity is very important for the object recognition [2][9]. So in this paper, we propose a feature which uses both intensity and gradient informations. Our feature, called "Co-Occurrence Feature (COF)", can treat the co-occurrence of salient regions in both of intensity domain and gradient domain. We use an extended image set that consists of original (intensity) image and oriented gradient images which are extracted from original images. COF is composed from a pair of arbitrary rectangles on arbitrary image channel in the extended image set. As a result of face/nonface classification experiments, it is confirmed that our feature has good classification performance, especially in the high true positive rate zone of ROC curves, the false detection rate is significantly better than Viola's HLF.