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Content based multispectral image retrieval using principal component analysis

Authors:

Hamed Shahbazi,

Mohsen Soryani,

Peyman KabiriAuthors Info & Claims

CIVR '08: Proceedings of the 2008 international conference on Content-based image and video retrieval

Pages 205 - 210

https://doi.org/10.1145/1386352.1386383

Published: 07 July 2008 Publication History

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Abstract

In most current image retrieval systems, the retrieval process is performed using similarity strategies applied on certain features in the image. This paper presents a novel method for multispectral image retrieval. The proposed method starts with calculation of two features and then it uses Principal Component Analysis (PCA) to extract principal components of the feature values. Later on, feature values of each image are exhibited by a linear combination of these principal components. In the proposed approach, two effective weight vectors are calculated for each image in the system. These two weight vectors are used efficiently in radiance and texture based retrieval process. The proposed method was performed and tested on a set of LANDSAT multispectral images from variant sceneries. Experimental results show the superior performance of this approach.

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Cited By

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Sakji-Nsibi SBenazza-Benyahia A(2009)Copula-based statistical models for multicomponent image retrieval in the wavelet transform domainProceedings of the 16th IEEE international conference on Image processing10.5555/1818719.1818807(253-256)Online publication date: 7-Nov-2009
https://dl.acm.org/doi/10.5555/1818719.1818807
Sakji-Nsibi SBenazza-Benyahia A(2009)Copula-based statistical models for multicomponent image retrieval in thewavelet transform domain2009 16th IEEE International Conference on Image Processing (ICIP)10.1109/ICIP.2009.5413483(253-256)Online publication date: Nov-2009
https://doi.org/10.1109/ICIP.2009.5413483

Index Terms

Content based multispectral image retrieval using principal component analysis
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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Information

Published In

CIVR '08: Proceedings of the 2008 international conference on Content-based image and video retrieval

July 2008

674 pages

ISBN:9781605580708

DOI:10.1145/1386352

General Chairs:
Jiebo Luo
Kodak Research Laboratories
,
Ling Guan
Ryerson University
,
Program Chairs:
Alan Hanjalic
Delft University of Technology
,
Mohan Kankanhalli
National University of Singapore
,
Ivan Lee
University of South Australia

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2008

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CIVR08: CIVR'08 - International Conference on Content-based Image and Video Retrieval

July 7 - 9, 2008

Niagara Falls, Canada

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Sakji-Nsibi SBenazza-Benyahia A(2009)Copula-based statistical models for multicomponent image retrieval in the wavelet transform domainProceedings of the 16th IEEE international conference on Image processing10.5555/1818719.1818807(253-256)Online publication date: 7-Nov-2009
https://dl.acm.org/doi/10.5555/1818719.1818807
Sakji-Nsibi SBenazza-Benyahia A(2009)Copula-based statistical models for multicomponent image retrieval in thewavelet transform domain2009 16th IEEE International Conference on Image Processing (ICIP)10.1109/ICIP.2009.5413483(253-256)Online publication date: Nov-2009
https://doi.org/10.1109/ICIP.2009.5413483

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Feature Selection Using Principal Component Analysis