Top

Neural Computing and Applications

Published in:

01-09-2012 | LSMS2010 and ICSEE 2010

DP-PMK: an improved pyramid matching kernel for approximating correspondences in high dimensions

Authors: Jun Zhang, Guangzhou Zhao, Hong Gu

Published in: Neural Computing and Applications | Issue 6/2012

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

As the feature dimension increases, the original pyramid matching kernel suffers from distortion factors that increase linearly with the feature dimension. This paper proposes a new method by consistently dividing 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 data set Caltech-101 and ETH-80 show that compared with other related algorithms which need hundreds of times of original computation time, dimension partition pyramid matching kernel only needs about 4–6 times less of original computation time to obtain the similar accuracy.

previous article Quantitative methods for detecting cerebral infarction from multiple channel EEG recordings

next article Investigation of different neural models for blood cell type identification

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Grauman K, Darrell T (2005) The pyramid match kernel: discriminative classification with sets of image features. In: Proceedings of the IEEE conference on ICCV, Beijing. IEEE, pp 1458–1465

Grauman K, Darrell T (2007) Approximate correspondences in high dimensions. Adv Neural Inf Process Syst 19:505

Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Proceedings of the IEEE conference on CVPR. IEEE, New York, pp 2169–2178

Liu Y, Wang X, Zha H (2008) Dimension amnesic pyramid match kernel. In: Proceedings of the 23rd national conference on artificial intelligence, Chicago. AAAI Press, pp 652–658

Zhang J, Zhao G, Gu H (2010) An improved pyramid matching kernel. In: 2010 International conference on life system modeling and simulation & 2010 international conference on intelligent computing for sustainable energy and environment (LSMS & ICSEE 2010), vol 6328, pp 52–61

Grauman K, Darrell T (2004) 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. IEEE, pp 220–227

Berg A, Berg T, Malik J (2005) Shape matching and object recognition using low distortion correspondences. In: Proceedings of the IEEE conference on CVPR, San Diego. IEEE, pp 26–33

Lyu S (2005) Mercer kernels for object recognition with local features. In: Proceedings of the IEEE conference on CVPR, San Diego. IEEE, pp 223–229

Charikar M (2002) Similarity estimation techniques from rounding algorithms. In: Proceedings of the 34th annual ACM symposium on theory of computing, Montreal. Association for Computing Machinery, pp 380–388

10.

Indyk P, Thaper N (2003) Fast image retrieval via embeddings. In: 3rd International workshop on statistical and computational theories of vision, Nice

11.

Agarwal P, Varadarajan K (2004) A near-linear algorithm for Euclidean bipartite matching. In: Symposium on computational geometry

12.

Grauman K, Darrell T (2007) The pyramid match kernel: efficient learning with sets of features. J Mach Learn Res 8:725–760MATH

13.

Grauman K (2006) Matching sets of features for efficient retrieval and recognition. MIT, Cambridge

14.

Kapoor A, Shenoy P, Tan D (2008) Combining brain computer interfaces with vision for object categorization. In: Proceedings of the IEEE conference on CVPR, Alaska. IEEE, pp 1–8

15.

Saenko K, Darrell T (2009) Filtering abstract senses from image search results. In: Proceedings of the 23rd annual conference on NIPS, Vancouver. MIT Press, pp 1–9

16.

Bosch A, Zisserman A, Munoz X (2007) Representing shape with a spatial pyramid kernel. In: Proceedings of the 6th ACM international conference on image and video retrieval, Amsterdam. Association for Computing Machinery, pp 401–408

17.

Yeh T, Lee J, Darrell T, MIT C (2007) Adaptive vocabulary forests for dynamic indexing and category learning. In: Proceedings of the IEEE international conference on computer vision, Rio de Janeiro. IEEE, pp 1–8

18.

Fu S, Shengyang G, Hou Z et al (2008) Multiple kernel learning from sets of partially matching image features. In: Proceedings of international conference on pattern recognition, Florida. IEEE Computer Society, pp 1–4

19.

He J, Chang S, Xie L (2008) Fast kernel learning for spatial pyramid matching. In: Proceedings of the IEEE conference on computer vision and pattern recognition, Alaska. IEEE, pp 1–7

20.

Shawe-Taylor J, Cristianini N (2004) Kernel methods for pattern analysis. Cambridge University Press, CambridgeCrossRef

21.

Lee JJ (2008, Apr) LIBPMK: a pyramid match toolkit. Available from: http://hdl.handle.net/1721.1/41070

22.

Fei-Fei L, Fergus R, Perona P (2004) Learning generative visual models from few training examples. In: Workshop on generative model based vision, Washington. Academic Press, p 178

23.

Harris C, Stephens M (1988) A combined corner and edge detector. In: Proceedings of the 4th Alvey vision conference, Manchester, pp 147–151

24.

Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110CrossRef

25.

Leibe B, Schiele B (2003) Analyzing appearance and contour based methods for object categorization. In: Proceedings of the IEEE conference on CVPR, Madison. IEEE, pp 409–415

26.

Ke Y, Sukthankar R (2004) PCA-SIFT: a more distinctive representation for local image descriptors. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition, Washington. IEEE, pp 503–516

Title: DP-PMK: an improved pyramid matching kernel for approximating correspondences in high dimensions
Authors: Jun Zhang
Guangzhou Zhao
Hong Gu
Publication date: 01-09-2012
Publisher: Springer-Verlag
Published in: Neural Computing and Applications / Issue 6/2012
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-012-0953-y

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 6/2012

Intelligent computing and applications (LSMS and ICSEE 2010)

Decentralized backstepping output-feedback control for stochastic interconnected systems with time-varying delays using neural networks

Protecting data privacy in growing neural gas

A feature binding computational model for multi-class object categorization and recognition

Reliability enhancement of power systems through a mean–variance approach

Training support vector data descriptors using converging linear particle swarm optimization

Premium Partner