NDLI: Comparative study of linear and nonlinear color model identification based optimal feature extraction

Content Provider	IEEE Xplore Digital Library
Author	Zhengmao Ye Mohamadian, H. Yongmao Ye
Copyright Year	2011
Description	Author affiliation: Liaoning Radio and Television Station, Shenyang, China (Yongmao Ye) \|\| Southern University, Baton Rouge, Louisiana, USA (Zhengmao Ye; Mohamadian, H.)
Abstract	Feature extraction involves detecting and separating fundamental portions of digital images, as well as simplifying a complex input data set into a set of features in a reduced order. It has a variety of practical applications in virtually all areas of pattern recognition and image processing disciplines. Dimensionality reduction is a major goal of feature extraction to retain the relevant information and eliminate the redundant and noisy information. To accurately describe a huge set of complex data with a lower dimensional space representation, optimality should be introduced into feature extraction, which includes both linear and nonlinear approaches, among which, both Principal Components Analysis (PCA) and Independent Component Analysis (ICA) are the most successful ones. Merits and drawbacks of these two linear and nonlinear approaches can be obtained via comparative analysis. The RGB model is an additive color model which displays a color by mixing intensity levels of red, green and blue components. The grayscale value is set when all three primary colors have the same intensity. Most digital images are displayed in RGB format, so optimal feature extraction should be conducted according to three individual components in the RGB model. In order to evaluate the two leading schemes of PCA and ICA for feature extraction, the quantitative metrics based on the information theory have been introduced so as to examine the qualities of optimal feature extraction approaches from both subjective and objective points of view.
Starting Page	97
Ending Page	102
File Size	1034810
Page Count	6
File Format	PDF
ISBN	9781457700446
e-ISBN	9781457700453
DOI	10.1109/CINTI.2011.6108479
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-21
Publisher Place	Hungary
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Independent Component Analysis Gray-scale Vectors Entropy Linear System Identification Covariance matrix Image color analysis RGB Color Model Quantatitive Metrics Feature extraction Principal Component Analysis Nonlinear System Identification Principal component analysis
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Statistical shape based multispectral image retrieval extracting Power Spectrum Vectors

An approach to loop-closing based on RGB-D images

Image authenticity implementing Principal Component Analysis (PCA)

A global covariance matrix based principal component analysis for speaker identification

Appearance based object recognition using two-dimensional optimal feature transform

A study of block-global feature based supervised image annotation

Palmprint Recognition Based on Two-Dimensional Methods

Entropy analysis applied to NFIR models

A polygonal line algorithm based nonlinear feature extraction method

Comparative study of linear and nonlinear color model identification based optimal feature extraction

Similar Documents

Statistical shape based multispectral image retrieval extracting Power Spectrum Vectors

An approach to loop-closing based on RGB-D images

Image authenticity implementing Principal Component Analysis (PCA)

A global covariance matrix based principal component analysis for speaker identification

Appearance based object recognition using two-dimensional optimal feature transform

A study of block-global feature based supervised image annotation

Palmprint Recognition Based on Two-Dimensional Methods

Entropy analysis applied to NFIR models

A polygonal line algorithm based nonlinear feature extraction method

Comparative study of linear and nonlinear color model identification based optimal feature extraction