Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Shape Matching and Object Recognition Using Low Distortion Correspondences
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Mercer Kernels for Object Recognition with Local Features
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
The Pyramid Match Kernel: Efficient Learning with Sets of Features
The Journal of Machine Learning Research
Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
Matching sets of features for efficient retrieval and recognition
Matching sets of features for efficient retrieval and recognition
Dimension amnesic pyramid match kernel
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
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The pyramid matching kernel (PMK) draws lots of researchers' attentions for its linear computational complexity while still having state-of-the-art performance. However, as the feature dimension increases, the original PMK suffers from distortion factors that increase linearly with the feature dimension. This paper proposes a new method called dimension partition PMK (DP-PMK) which only increases little couples of the original PMK's computation time. But DP-PMK still catches up with other proposed strategies. The main idea of the method is to consistently divide the feature space into two subspaces while generating several levels. In each subspace of the level, the original pyramid matching is used. Then a weighted sum of every subspace at each level is made as the final measurement of similarity. Experiments on dataset Caltech-101 show its impressive performance: compared with other related algorithms which need hundreds of times of original computational time, DP-PMK needs only about 4-6 times of original computational time to obtain the same accuracy.