Fast image classification algorithms based on random weights networks

Authors:
Feilong Cao;Jianwei Zhao;Bo Liu
Affiliations:
Department of Information and Mathematics Sciences, China Jiliang University, Hangzhou, Zhejiang Province, P.R. China;Department of Information and Mathematics Sciences, China Jiliang University, Hangzhou, Zhejiang Province, P.R. China;Department of Information and Mathematics Sciences, China Jiliang University, Hangzhou, Zhejiang Province, P.R. China
Venue:
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Year:
2013

Citing 15
Cited 0

On the approximate realization of continuous mappings by neural networks

Neural Networks
Approximation capabilities of multilayer feedforward networks

Neural Networks
The nature of statistical learning theory

The nature of statistical learning theory
Face Recognition by Support Vector Machines

FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Face recognition: A literature survey

ACM Computing Surveys (CSUR)
Eigenfaces for recognition

Journal of Cognitive Neuroscience
Human face recognition based on multidimensional PCA and extreme learning machine

Pattern Recognition
Batch mode active learning for multi-label image classification with informative label correlation mining

WACV '12 Proceedings of the 2012 IEEE Workshop on the Applications of Computer Vision
Using Support Vector Machines to Enhance the Performance of Bayesian Face Recognition

IEEE Transactions on Information Forensics and Security
Wavelet Feature Selection for Image Classification

IEEE Transactions on Image Processing
A hybrid linear/nonlinear training algorithm for feedforward neural networks

IEEE Transactions on Neural Networks
Support vector machines for histogram-based image classification

IEEE Transactions on Neural Networks
Approximation capability to functions of several variables, nonlinear functionals, and operators by radial basis function neural networks

IEEE Transactions on Neural Networks
Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems

IEEE Transactions on Neural Networks
Stochastic choice of basis functions in adaptive function approximation and the functional-link net

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Up to now, rich and varied information, such as networks, multimedia information, especially images and visual information, has become an important part of information retrieval, in which video and image information has been an important basis. In recent years, an effective learning algorithm for standard feed-forward neural networks (FNNs), which can be used classifier and called random weights networks (RWN), has been extensively studied. This paper addresses the image classification algorithms using the algorithm. A new algorithm of image classification based on the RWN and principle component analysis (PCA) is proposed. The proposed algorithm includes significant improvements in classification rate, and the extensive experiments are performed using challenging databases. Compared with some traditional approaches, the new method has superior performances on both classification rate and running time.