Abstract
In the field of object recognition in computer vision, feature point clustering algorithm has become an important part of the object recognition. After getting the object feature points, we make the feature points in clustering in the use of GG-RNN clustering algorithm, to achieve multi-part of the object clustering or the multi-object clustering. And the GG-RNN clustering algorithm we propose innovatively, is merged with the grayscale and gradient information based on Euclidean distance in the similarity calculation. Compared with the distance description of basic RNN algorithm, the similarity calculation of high-dimensional description of GG-RNN will improve the accuracy of the clustering in different conditions.
Keywords
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
MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967)
De Rham, C.: La classification hiérarchique ascendante selon la méthode des voisins réciproques. Les Cahiers de l’Analyse des Données 5(2), 135–144 (1980)
Leibe, B., Mikolajczyk, K., Schiele, B.: Efficient clustering and matching for object class recognition. In: BMVC (2006)
Huang, S., Li, X.: Based on Improving the Effect of RNN Clustering Algorithm Research. Journal of Taiyuan Normal University 11(2), 72–75 (2012)
López-Sastre, R.J., Oñoro-Rubio, D., Gil-Jiménez, P., Maldonado-Bascón, S.: Fast reciprocal nearest neighbors clustering. Signal Process. 92(1), 270–275 (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhou, Z., Shen, D., Kang, L., Wang, J. (2013). A Feature Point Clustering Algorithm Based on GG-RNN. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7951. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39065-4_56
Download citation
DOI: https://doi.org/10.1007/978-3-642-39065-4_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39064-7
Online ISBN: 978-3-642-39065-4
eBook Packages: Computer ScienceComputer Science (R0)