Real-time image recognition with the parallel directed enumeration method

Authors:
Andrey V. Savchenko
Affiliations:
National Research University Higher School of Economics, Nizhniy Novgorod, Russian Federation
Venue:
ICVS'13 Proceedings of the 9th international conference on Computer Vision Systems
Year:
2013

Citing 9
Cited 1

Two algorithms for nearest-neighbor search in high dimensions

STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Computer Vision: A Modern Approach

Computer Vision: A Modern Approach
Shape Indexing Using Approximate Nearest-Neighbour Search in High-Dimensional Spaces

CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Histograms of Oriented Gradients for Human Detection

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
M-Chord: a scalable distributed similarity search structure

InfoScale '06 Proceedings of the 1st international conference on Scalable information systems
Distributed similarity search in high dimensions using locality sensitive hashing

Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Handbook of Face Recognition

Handbook of Face Recognition
Directed enumeration method in image recognition

Pattern Recognition
Statistical recognition of a set of patterns using novel probability neural network

ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition

Pattern recognition and increasing of the computational efficiency of a parallel realization of the probabilistic neural network with homogeneity testing

Optical Memory and Neural Networks

Quantified Score

Hi-index	0.00

Visualization

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.