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On “shapes” of colors for content-based image retrieval

Authors:

Renato O. Stehling,

Mario A. Nascimento,

Alexandre X. FalcãoAuthors Info & Claims

MULTIMEDIA '00: Proceedings of the 2000 ACM workshops on Multimedia

Pages 171 - 174

https://doi.org/10.1145/357744.361911

Published: 04 November 2000 Publication History

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Abstract

Color is a commonly used feature for realizing content-based image retrieval (CBIR). Towards this goal, this paper presents a new approach for CBIR which is based on well known and widely used color histograms. Contrasting to previous approaches, such as using a single color histogram for the whole image, or local color histograms for a fixed number of image cells, the one we propose (named Color Shape) uses a variable number of histograms, depending only on the actual number of colors present in the image. Our experiments using a large set of heterogeneous images and pre-defined query/answer sets show that the Color Shape approach offers good retrieval quality with relatively low space overhead, outperforming previous approaches.

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Joseph IKhan MLuo H(2020)Orange Leaf Diseases Identification Using Digital Image ProcessingArtificial Intelligence Algorithms and Applications10.1007/978-981-15-5577-0_27(360-378)Online publication date: 26-May-2020
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Yu HLiu JSun HWang ZZhang H(2019)GetNet: Get Target Area for Image Pairing2019 International Conference on Image and Vision Computing New Zealand (IVCNZ)10.1109/IVCNZ48456.2019.8960995(1-6)Online publication date: Dec-2019
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Singh NSingh KSinha A(2012)A Novel Approach for Content Based Image RetrievalProcedia Technology10.1016/j.protcy.2012.05.0374(245-250)Online publication date: 2012
https://doi.org/10.1016/j.protcy.2012.05.037
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Published In

MULTIMEDIA '00: Proceedings of the 2000 ACM workshops on Multimedia

November 2000

248 pages

ISBN:1581133111

DOI:10.1145/357744

Chairmen:
Shahram Ghandeharizadeh
Univ. of Southern California
,
Shih-Fu Chang
Columbia Univ., New York, NY
,
Stephen Fischer
GMD-IPSI, Germany
,
Joseph Konstan
Univ. of Minnesota
,
Klara Nahrstedt
Univ. of Illinois, Urbana-Champaign

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: 04 November 2000

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MM00

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MM00: ACM Multimedia 2000

October 30 - November 3, 2000

California, Los Angeles, USA

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Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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Cited By

View all

Joseph IKhan MLuo H(2020)Orange Leaf Diseases Identification Using Digital Image ProcessingArtificial Intelligence Algorithms and Applications10.1007/978-981-15-5577-0_27(360-378)Online publication date: 26-May-2020
https://doi.org/10.1007/978-981-15-5577-0_27
Yu HLiu JSun HWang ZZhang H(2019)GetNet: Get Target Area for Image Pairing2019 International Conference on Image and Vision Computing New Zealand (IVCNZ)10.1109/IVCNZ48456.2019.8960995(1-6)Online publication date: Dec-2019
https://doi.org/10.1109/IVCNZ48456.2019.8960995
Singh NSingh KSinha A(2012)A Novel Approach for Content Based Image RetrievalProcedia Technology10.1016/j.protcy.2012.05.0374(245-250)Online publication date: 2012
https://doi.org/10.1016/j.protcy.2012.05.037
Wang JHua X(2011)Interactive Image Search by Color MapACM Transactions on Intelligent Systems and Technology10.1145/2036264.20362763:1(1-23)Online publication date: 1-Oct-2011
https://dl.acm.org/doi/10.1145/2036264.2036276
Xu Xinzheng Ding Shifei Jiang Haifeng Zhao Zuopeng Wang Chun Wang Qian (2010)Image retrieval based on image decomposition and weight assignments2010 2nd International Conference on Computer Engineering and Technology10.1109/ICCET.2010.5485456(V4-488-V4-491)Online publication date: Apr-2010
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Chen MChen S(2009)MMIRArtificial Intelligence for Maximizing Content Based Image Retrieval10.4018/978-1-60566-174-2.ch016(371-387)Online publication date: 2009
https://doi.org/10.4018/978-1-60566-174-2.ch016
Palma-Amestoy RGuerrero PRuiz-Del-Solar JGarretón C(2009)Bayesian Spatiotemporal Context Integration Sources in Robot Vision SystemsRoboCup 2008: Robot Soccer World Cup XII10.1007/978-3-642-02921-9_19(212-224)Online publication date: 24-Jun-2009
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Oussalah M(2008)Content Based Image Retrieval: Review of State of Art and Future Directions2008 First Workshops on Image Processing Theory, Tools and Applications10.1109/IPTA.2008.4743799(1-10)Online publication date: Nov-2008
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