Abstract
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Grauman, K., Darrell, T.: The pyramid match kernel: Discriminative classification with sets of image features. In: Proceedings of the IEEE International Conference on Computer Vision, Beijing, China, vol. 2 (2005)
Grauman, K., Darrell, T.: Approximate correspondences in high dimensions. In: Scholkopf, B., Platt, J.C., Hofmann, T. (eds.) Advances in Neural Information Processing Systems, Cambridge, MA (2007)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, New York City, NY, vol. 2 (2006)
Liu, Y., Wang, X., Zha, H.: Dimension amnesic pyramid match kernel. In: Proceedings of the 23rd National Conference on Artificial Intelligence, Chicago, vol. 2, pp. 652–658 (2008)
Grauman, K., Darrell, T.: Fast contour matching using approximate earth mover’s distance. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington D.C., vol. 1 (2004)
Berg, A., Berg, T., Malik, J.: Shape matching and object recognition using low distortion correspondences. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, vol. 1 (2005)
Lyu, S.: Mercer kernels for object recognition with local features. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, vol. 2 (2005)
Grauman, K., Darrell, T.: The pyramid match kernel: Efficient learning with sets of features. Journal of Machine Learning Research 8, 725–760 (2007)
Grauman, K.: Matching sets of features for efficient retrieval and recognition. PhD thesis. MIT (2006)
Kapoor, A., Shenoy, P., Tan, D.: Combining brain computer interfaces with vision for object categorization. In: IEEE Conference on Computer Vision and Pattern Recognition, Alaska, pp. 1–8 (2008)
Saenko, K., Darrell, T.: Filtering Abstract Senses From Image Search Results. MIT CSAIL, Cambridge (2009)
Bosch, A., Zisserman, A., Munoz, X.: Representing shape with a spatial pyramid kernel. In: Proceedings of the 6th ACM International Conference on Image and Video Retrieval, Amsterdam, p. 408 (2007)
Yeh, T., Lee, J., Darrell, T., MIT, C.: Adaptive vocabulary forests for dynamic indexing and category learning. In: Proceedings of the IEEE International Conference on Computer Vision, Rio de Janeiro, Brazil, pp. 1–8 (2007)
Fu, S., ShengYang, G., Hou, Z., Liang, Z., Tan, M.: Multiple kernel learning from sets of partially matching image features. In: Proceedings of the IEEE International Conference on Pattern Recognition, Florida, pp. 1–4 (2008)
He, J., Chang, S., Xie, L.: Fast kernel learning for spatial pyramid matching. In: IEEE Conference on Computer Vision and Pattern Recognition, Alaska, pp. 1–7 (2008)
Shawe-Taylor, J., Cristianini, N.: Kernel methods for pattern analysis. Cambridge University Press, Cambridge (2004)
Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. In: Workshop on Generative Model Based Vision, Washington, D.C. (2004)
Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of the 4th Alvey Vision Conference, Manchester, UK, vol. 15, pp. 147–151 (1988)
Lowe, D.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, J., Zhao, G., Gu, H. (2010). An Improved Pyramid Matching Kernel. In: Li, K., Fei, M., Jia, L., Irwin, G.W. (eds) Life System Modeling and Intelligent Computing. ICSEE LSMS 2010 2010. Lecture Notes in Computer Science, vol 6328. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15621-2_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-15621-2_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15620-5
Online ISBN: 978-3-642-15621-2
eBook Packages: Computer ScienceComputer Science (R0)