Adaptive video image recognition system using a committee machine

Authors:
A. V. Savchenko
Affiliations:
National Research University Higher School of Economics, Nizhny Novgorod, Russia
Venue:
Optical Memory and Neural Networks
Year:
2012

Citing 4
Cited 2

Texture Features for Browsing and Retrieval of Image Data

IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
Pattern Recognition, Fourth Edition

Pattern Recognition, Fourth Edition
Directed enumeration method in image recognition

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
About neural-network algorithms application in viseme classification problem with face video in audiovisual speech recognition systems

Optical Memory and Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

An ensemble of classifiers has been built to solve the problem of video image recognition. The paper offers a way to estimate the a posteriori probability of an image belonging to a particular class in the case of an arbitrary distance and nearest neighbor method. The estimation is shown to be equivalent to the optimal naive Bayesian estimate given the Kullback-Leibler discrimination being used as similarity measure. The block diagram of a video image recognition system is presented. The system features automatic adaptation of the list of images of identical objects which is fed to the committee machine input. The system is tested in face recognition task using popular data sets (FERET, AT&T, Yale) and the results are discussed.