Neural Networks
Machine Learning
Neural networks for pattern recognition
Neural networks for pattern recognition
An optimal algorithm for approximate nearest neighbor searching fixed dimensions
Journal of the ACM (JACM)
Analysis of an Algorithm for Finding Nearest Neighbors in Euclidean Space
ACM Transactions on Mathematical Software (TOMS)
Optimal Expected-Time Algorithms for Closest Point Problems
ACM Transactions on Mathematical Software (TOMS)
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)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
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
Using the Language MC# for the Implementation of a Parallel Pattern Classifier
Cybernetics and Systems Analysis
Local invariant feature detectors: a survey
Foundations and Trends® in Computer Graphics and Vision
Similarity grid for searching in metric spaces
DELOS'04 Proceedings of the 6th Thematic conference on Peer-to-Peer, Grid, and Service-Orientation in Digital Library Architectures
Directed enumeration method in image recognition
Pattern Recognition
SISAP'12 Proceedings of the 5th international conference on Similarity Search and Applications
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
Adaptive video image recognition system using a committee machine
Optical Memory and Neural Networks
Real-time image recognition with the parallel directed enumeration method
ICVS'13 Proceedings of the 9th international conference on Computer Vision Systems
Optical Memory and Neural Networks
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The research subject is the computational complexity of the probabilistic neural network (PNN) in the pattern recognition problem for large model databases. We examined the following methods of increasing the efficiency of a neural-network classifier: a parallel multithread realization, reducing the PNN to a criterion with testing of homogeneity of feature histograms of input and reference images, approximate nearest-neighbor analyses (Best-Bin First, directed enumeration methods). The approach was tested in facial-recognition experiments with FERET dataset.