Abstract
The parallel computing algorithms are explored to improve the efficiency of image recognition with large database. The novel parallel version of the directed enumeration method (DEM) is proposed. The experimental study results in face recognition problem with FERET and Essex datasets are presented. We compare the performance of our parallel DEM with the original DEM and parallel implementations of the nearest neighbor rule and conventional Best Bin First (BBF) k-d tree. It is shown that the proposed method is characterized by increased computing efficiency (2-10 times in comparison with exhaustive search and the BBF) and lower error rate than the original DEM.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Forsyth, D.A., Ponce, J.: Computer Vision: A Modern Approach. Prentice Hall (2011)
Li, S.Z., Jain, A.K. (eds.): Handbook of Face Recognition, 2nd edn. Springer (2011)
Beis, J., Lowe, D.G.: Shape indexing using approximate nearest-neighbour search in high dimensional spaces. In: Conference on Computer Vision and Pattern Recognition, pp. 1000–1006 (1997)
Savchenko, A.V.: Directed enumeration method in image recognition. Pattern Recognition 45(8), 2952–2961 (2012)
Liu, T., Moore, A.W., Gray, A., Yang, K.: An Investigation of Practical Approximate Nearest Neighbor Algorithms. In: NIPS-2004, pp. 825–832 (2004)
Kleinberg, J.: Two algorithms for nearest-neighbor search in high dimensions. In: Twenty-Ninth Annual ACM Symposium on Theory of Computing, pp. 599–608 (1997)
Novak, D., Zezula, P.: M-Chord: A Scalable Distributed Similarity Search Structure. In: Infoscale, pp. 149–160 (2006)
Haghani, P., Michel, S., Aberer, K.: Distributed similarity search in high dimensions using locality sensitive hashing. In: EDBT 2009, pp. 744–755 (2009)
Savchenko, A.V.: Face Recognition in Real-Time Applications: Comparison of Directed Enumeration Method and K-d Trees. In: Aseeva, N., Babkin, E., Kozyrev, O. (eds.) BIR 2012. LNBIP, vol. 128, pp. 187–199. Springer, Heidelberg (2012)
Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: Conference on Computer Vision and Pattern Recognition, pp. 886–893 (2005)
Savchenko, A.V.: Statistical Recognition of a Set of Patterns Using Novel Probability Neural Network. In: Mana, N., Schwenker, F., Trentin, E. (eds.) ANNPR 2012. LNCS, vol. 7477, pp. 93–103. Springer, Heidelberg (2012)
Sneath, P., Sokal, R.: Numerical Taxonomy: The Principles and Practice of Numerical Classification. Freeman (1973)
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
Savchenko, A.V. (2013). Real-Time Image Recognition with the Parallel Directed Enumeration Method. In: Chen, M., Leibe, B., Neumann, B. (eds) Computer Vision Systems. ICVS 2013. Lecture Notes in Computer Science, vol 7963. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39402-7_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-39402-7_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39401-0
Online ISBN: 978-3-642-39402-7
eBook Packages: Computer ScienceComputer Science (R0)