NDLI: Linear principal transformation: toward locating features in N-dimensional image space

Content Provider	Springer Nature Link
Author	Dehshibi, Mohammad Mahdi Fazlali, Mahmood Shanbehzadeh, Jamshid
Copyright Year	2013
Abstract	Projection Functions have been widely used for facial feature extraction and optical/handwritten character recognition due to their simplicity and efficiency. Because these transformations are not one-to-one, they may result in mapping distinct points into one point, and consequently losing detailed information. Here, we solve this problem by defining an N-dimensional space to represent a single image. Then, we propose a one-to-one transformation in this new image space. The proposed method, which we referred to as Linear Principal Transformation (LPT), utilizes Eigen analysis to extract the vector with the highest Eigenvalue. Afterwards, extrema in this vector were analyzed to extract the features of interest. In order to evaluate the proposed method, we performed two sets of experiments on facial feature extraction and optical character recognition in three different data sets. The results show that the proposed algorithm outperforms the observed algorithms in the paper and achieves accuracy from 1.4 % up to 14 %, while it has a comparable time complexity and efficiency.
Starting Page	2249
Ending Page	2273
Page Count	25
File Format	PDF
ISSN	13807501
Journal	Multimedia Tools and Applications
Volume Number	72
Issue Number	3
e-ISSN	15737721
Language	English
Publisher	Springer US
Publisher Date	2013-06-09
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Character recognition Facial feature extraction Image space Linear principal transformation Projection function Multimedia Information Systems Computer Communication Networks Data Structures, Cryptology and Information Theory Special Purpose and Application-Based Systems
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Hardware and Architecture Software Media Technology

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