Paper Info
Title
Optimal Matrix Transform For The Extraction Of Algebraic Features From Images
Abstract
A new algebraic feature extraction method for image recognition is presented. The optimal transform of image matrices is proposed to extract the features from images. The Frobenius norm of matrices is first introduced as a measure of the distance between two matrices. Based on this, the within-class and between-class distances of image samples are defined. The ratio of the between-class and within-class distances of the transformed image sample set is taken as the criterion function J(T). The optimal transform matrix T is calculated by maximizing J(T) under some constraints. Experiments have been conducted to recognize both human face and handwritten character images. These results indicate that the algebraic features extracted by the present method possess a very strong discriminant power. An important conclusion about the present method is that the traditional linear discriminant method can be considered as a special case of the present feature method when image samples have only one column of vectors.
Year
DOI
Venue
1996
10.1142/S0218001496000244
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
Keywords
Field
DocType
feature extraction, image recognition, character recognition, human face recognition, linear discriminant analysis
Top-hat transform,Algebraic number,Pattern recognition,Discriminant,Matrix (mathematics),Algorithm,Feature extraction,Matrix norm,Artificial intelligence,Linear discriminant analysis,Transformation matrix,Mathematics
Journal
Volume
Issue
ISSN
10
4
0218-0014
Citations 
PageRank 
References 
2
0.51
2
Authors
3
Name
Order
Citations
PageRank
Ke Liu116416.01
Yea-shuan Huang247979.42
Ching Y. Suen375691127.54