An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Journal of the ACM (JACM)
Evaluation of Interest Point Detectors
International Journal of Computer Vision - Special issue on a special section on visual surveillance
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition with Local Features: the Kernel Recipe
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Shared Features for Scalable Appearance-Based Object Recognition
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
PCA-SIFT: a more distinctive representation for local image descriptors
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Sharing features: efficient boosting procedures for multiclass object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Biologically motivated perceptual feature: generalized robust invariant feature
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Robust model-based scene interpretation by multilayered context information
Computer Vision and Image Understanding
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
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In this paper, we present a new scalable 3D object representation and learning method to recognize many objects. Scalability is one of the important issues in object recognition to reduce memory and recognition time. The key idea of scalable representation is to combine a feature sharing concept with view clustering in part-based object representation (especially a CFCM: common frame constellation model). In this representation scheme, we also propose a fully automatic learning method: appearance-based automatic feature clustering and sequential construction of view-tuned CFCMs from labeled multi-views and multi-objects. We applied this learning scheme to 40 objects with 216 training views. Experimental results show the scalable learning results in almost constant recognition performance relative to the number of objects.