research-article

Category specific SIFT descriptor and its combination with color information for content-based image retrieval

Authors:

Xiao HanAuthors Info & Claims

ICIS '09: Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human

Pages 685 - 690

https://doi.org/10.1145/1655925.1656049

Published: 24 November 2009 Publication History

Abstract

The Scale-Invariant Feature Transform (SIFT), which is invariant to image scaling and rotation, and partially invariant to change in illumination and 3D camera viewpoint, is proved to be one of the best local feature descriptors. However, the number of SIFT features for one image often varies from tens to thousands, and matching such number size of SIFT keypoints between two arbitrary images can bring vast computational costs. We proposed a method to reduce original SIFT features by filtering out those SIFT features which can truly represent category properties-Category Specific SIFT Descriptor (CSSD) based on clustering and statistics and then using them to reduce each image SIFT features. On the other hand, SIFT features are mainly designed for gray image rather than color image applications. Whereas the color information provides important contents as well, and it is rotation and scale invariant which never violates SIFT properties. Thereby we presented a content-based image retrieval system in which the reduced SIFT features are combined with color information via a loosely-coupled style. Comparing to some existing color-SIFT descriptors, this enables the system to easily control the weight between SIFT features and color information and never enlarge the number of SIFT features. Finally, extensive experiments showed that the proposed approach outperforms the original SIFT and the optimal color features from four popular approaches for combination and the optimal combinational weights for 10 image categories based on Corel image database are obtained.

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Cited By

Sun CLi CZhu Y(2019)A Novel Convolutional Neural Network Based Localization System for Monocular ImagesInternational Journal of Software Science and Computational Intelligence10.4018/IJSSCI.201904010311:2(38-50)Online publication date: Apr-2019
https://doi.org/10.4018/IJSSCI.2019040103
Alzu’bi AAmira ARamzan NJaber T(2016)Improving content-based image retrieval with compact global and local multi-featuresInternational Journal of Multimedia Information Retrieval10.1007/s13735-016-0109-45:4(237-253)Online publication date: 27-Sep-2016
https://doi.org/10.1007/s13735-016-0109-4
Alzu'bi AAmira ARamzan NJaber T(2015)Robust fusion of color and local descriptors for image retrieval and classification2015 International Conference on Systems, Signals and Image Processing (IWSSIP)10.1109/IWSSIP.2015.7314224(253-256)Online publication date: Sep-2015
https://doi.org/10.1109/IWSSIP.2015.7314224
Show More Cited By

Index Terms

Category specific SIFT descriptor and its combination with color information for content-based image retrieval
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision representations
        Image representations
2. Information systems
  1. Information retrieval

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cover image ACM Other conferences

ICIS '09: Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human

November 2009

1479 pages

ISBN:9781605587103

DOI:10.1145/1655925

Conference Chairs:
Sungwon Sohn
ETRI, Korea
,
Kyhyun Um
Dongguk University, Korea
,
Franz I. S. Ko
Dongguk University, Korea/IBC, UK
,
Kae Dal Kwack
Hanyang University, Korea
,
Jong Hyung Lee
Gumi Electronics & Information Technology Research Institute, Korea
,
Gang Kou
University of Electronic Science and Technology of China, China
,
Kiyoshi Nakamura
Waseda University, Japan
,
ACM Fong
Nanyang Technological University, Singapore
,
Patrick Ma
The Hong Kong Polytechnic University, Hongkong
,
Ling Chen
Zhejiang University, China
,
Soonwook Hwang
KISTI, Korea
,
Kyungeun Cho
Dongguk University, Korea
,
Shigeo Kawata
Utsunomiya University, Japan

Copyright © 2009 ACM.

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: 24 November 2009

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Funding Sources

Natural Science Foundation of Beijing Institute of Technology
National Natural Science Foundation of China
Pre-research Project of the '11th Five-Year-Plan' of China

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ICIS '09

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ICIS '09: The 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human

November 24 - 26, 2009

Seoul, Korea

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Cited By

Sun CLi CZhu Y(2019)A Novel Convolutional Neural Network Based Localization System for Monocular ImagesInternational Journal of Software Science and Computational Intelligence10.4018/IJSSCI.201904010311:2(38-50)Online publication date: Apr-2019
https://doi.org/10.4018/IJSSCI.2019040103
Alzu’bi AAmira ARamzan NJaber T(2016)Improving content-based image retrieval with compact global and local multi-featuresInternational Journal of Multimedia Information Retrieval10.1007/s13735-016-0109-45:4(237-253)Online publication date: 27-Sep-2016
https://doi.org/10.1007/s13735-016-0109-4
Alzu'bi AAmira ARamzan NJaber T(2015)Robust fusion of color and local descriptors for image retrieval and classification2015 International Conference on Systems, Signals and Image Processing (IWSSIP)10.1109/IWSSIP.2015.7314224(253-256)Online publication date: Sep-2015
https://doi.org/10.1109/IWSSIP.2015.7314224
Korinke CRabbath MLamken DBoll SJaimes ASebe NBoujemaa NGatica-Perez DShamma DWorring MZimmermann R(2013)A tool for catching back your preferred videos from physical collagesProceedings of the 21st ACM international conference on Multimedia10.1145/2502081.2502250(421-422)Online publication date: 21-Oct-2013
https://dl.acm.org/doi/10.1145/2502081.2502250

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