One-class SVM applied to identification of diffractive optical variable image

Authors:
Jing Shao;Xinyu Chen;Ping Guo
Affiliations:
School of Beijing Normal University, Beijing, China;School of Beijing Normal University, Beijing, China;School of Beijing Normal University, Beijing, China
Venue:
ASID'09 Proceedings of the 3rd international conference on Anti-Counterfeiting, security, and identification in communication
Year:
2009

Citing 6
Cited 0

The nature of statistical learning theory

The nature of statistical learning theory
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Advances in kernel methods: support vector learning

Advances in kernel methods: support vector learning
Estimating the Support of a High-Dimensional Distribution

Neural Computation
A study of regularized Gaussian classifier in high-dimension small sample set case based on MDL principle with application to spectrum recognition

Pattern Recognition
An introduction to kernel-based learning algorithms

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose a method by engaging the One Class Support Vector Machine (OC-SVM) in the identification of Diffractive Optically Variable Images (DOVIs). OC-SVM, as a special SVM, can solve the problems of high-dimensional data sets and small sample size (SSS) with positive and negative unbalance training data. Image feature matrix is built by extracting image features from texture aspects. OC-SVM can be trained with the high-dimensional matrix directly, and does not have to reduce the dimensionality of feature matrix as the usual methods. The experiment results show the effectiveness of the proposed approach against Linear Discriminant Analysis. Considering time cost and correct classification rate, OC-SVM is suitable for the identification of DOVIs.