Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Real-Time Pattern Matching Using Projection Kernels
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Photo tourism: exploring photo collections in 3D
ACM SIGGRAPH 2006 Papers
Scalable Recognition with a Vocabulary Tree
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Large head movement tracking using sift-based registration
Proceedings of the 15th international conference on Multimedia
Speeded-Up Robust Features (SURF)
Computer Vision and Image Understanding
Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
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
Matching optimization based on KLT algorithm in natural feature tracking of augmented reality
Proceedings of the 11th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and its Applications in Industry
A feature compression scheme for large scale image retrieval systems
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
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Invariant feature descriptors such as SIFT and GLOH have been demonstrated to be very robust for image matching and object recognition. However, such descriptors are typically of high dimensionality, e.g. 128-dimension in the case of SIFT. This limits the performance of feature matching techniques in terms of speed and scalability. A new compact feature descriptor, called Kernel Projection Based SIFT (KPB-SIFT), is presented in this paper. Like SIFT, our descriptor encodes the salient aspects of image information in the feature point's neighborhood. However, instead of using SIFT's smoothed weighted histograms, we apply kernel projection techniques to orientation gradient patches. The produced KPB-SIFT descriptor is more compact as compared to the state-of-the-art, does not require pre-training step needed by PCA based descriptors, and shows superior advantages in terms of distinctiveness, invariance to scale, and tolerance of geometric distortions. We extensively evaluated the effectiveness of KPB-SIFT with datasets acquired under varying circumstances.