Paper Info
Title
A novel feature extraction method for image recognition based on similar discriminant function (SDF)
Abstract
The extraction of image features is the most fundamental and important problem in image recognition. In this paper, a similarity measure of matrices is first presented, and a similar discriminant function (SDF) of images is established. Based on the discriminant function, we further propose a novel feature extraction method for image recognition. For each class of training image samples, an optimal projection axis maximizing the similarity among these training image samples for the class is calculated. Unlike the common methods of feature extraction, we extract a projective feature vector for a training image sample by projecting the image on the optimal projection axis of the class itself, and a set of projective feature vectors for a testing image sample by projecting the image on all the optimal projection axes. Finally, a hierarchical classifier in the optimal discriminant space is designed to recognize images. In order to test the efficiency of our method, it is used to recognize human faces and English characters. Experimental results have shown that our method has good recognition performance, and the extracted projective feature vectors contain more recognition information than commonly used image features.
Year
DOI
Venue
1993
10.1016/0031-3203(93)90093-C
Pattern Recognition
Keywords
Field
DocType
Feature extraction,Similar discriminant function,Image recognition,Human face recognition,Hierarchical classifier,Pattern recognition
Feature detection (computer vision),Similarity measure,Artificial intelligence,Hierarchical classifier,Discriminant function analysis,Computer vision,Feature vector,Pattern recognition,Feature (computer vision),Discriminant,Feature extraction,Mathematics,Machine learning
Journal
Volume
Issue
ISSN
26
1
0031-3203
Citations 
PageRank 
References 
6
2.62
11
Authors
3
Name
Order
Citations
PageRank
Yong-Qing Cheng131057.01
Ke Liu224745.43
Jing-yu Yang36061345.83