Article

An effective region-based image retrieval framework

Authors:

Hong-Jiang Zhang,

Bo ZhangAuthors Info & Claims

MULTIMEDIA '02: Proceedings of the tenth ACM international conference on Multimedia

Pages 456 - 465

https://doi.org/10.1145/641007.641106

Published: 01 December 2002 Publication History

Abstract

We present a region-based image retrieval framework that integrates efficient region-based representation in terms of storage and retrieval and effective on-line learning capability. The framework consists of methods for image segmentation and grouping, indexing using modified inverted file, relevance feedback, and continuous learning. By exploiting a vector quantization method, a compact region-based image representation is achieved. Based on this representation, an indexing scheme similar to the inverted file technology is proposed. In addition, it supports relevance feedback based on the vector model with a weighting scheme. A continuous learning strategy is also proposed to enable the system to self improve. Experimental results on a database of 10,000 general-purposed images demonstrate the efficiency and effectiveness of the proposed framework.

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Published In

cover image ACM Conferences

MULTIMEDIA '02: Proceedings of the tenth ACM international conference on Multimedia

December 2002

683 pages

ISBN:158113620X

DOI:10.1145/641007

Conference Chair:
Lawrence Rowe
UC Berkeley
,
General Chair:
Bernard Merialdo
Institut EURECOM
,
Program Chairs:
Max Muhlhauser
TU Darmstadt
,
Keith Ross
Institut EURECOM
,
Nevenka Dimitrova
Phillips Research

Copyright © 2002 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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Published: 01 December 2002

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December 1 - 6, 2002

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Kim CChung C(2018)XMageInformation Processing and Management: an International Journal10.1016/j.ipm.2005.01.00742:2(484-502)Online publication date: 29-Dec-2018
https://dl.acm.org/doi/10.1016/j.ipm.2005.01.007
Shyu MChen SChen MZhang CSarinnapakorn K(2018)Capturing high-level image concepts via affinity relationships in image database retrievalMultimedia Tools and Applications10.1007/s11042-006-0059-632:1(73-92)Online publication date: 30-Dec-2018
https://dl.acm.org/doi/10.1007/s11042-006-0059-6
Zhu SZou LFang B(2018)Content based image retrieval via a transductive modelJournal of Intelligent Information Systems10.1007/s10844-013-0257-442:1(95-109)Online publication date: 28-Dec-2018
https://dl.acm.org/doi/10.1007/s10844-013-0257-4
Uluwitige DGeva SZuccon GChandran VChappell T(2016)Effective User Relevance Feedback for Image Retrieval with Image SignaturesProceedings of the 21st Australasian Document Computing Symposium10.1145/3015022.3015034(49-56)Online publication date: 5-Dec-2016
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Pal R(2014)Computational Models of Visual AttentionResearch Developments in Computer Vision and Image Processing10.4018/978-1-4666-4558-5.ch004(54-76)Online publication date: 2014
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Anusha THemavathi NMahantesh KChetana R(2014)An investigation of combining gradient descriptor and diverse classifiers to improve object taxonomy in very large image dataset2014 International Conference on Contemporary Computing and Informatics (IC3I)10.1109/IC3I.2014.7019774(581-585)Online publication date: Nov-2014
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González-Díaz IBenois-Pineau JBuso VBoujut H(2014)Fusion of Multiple Visual Cues for Object Recognition in VideosFusion in Computer Vision10.1007/978-3-319-05696-8_4(79-107)Online publication date: 26-Mar-2014
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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
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Yang LGeng BHanjalic AHua XLi SGao XSebe N(2010)Contextual image retrieval modelProceedings of the ACM International Conference on Image and Video Retrieval10.1145/1816041.1816100(406-413)Online publication date: 5-Jul-2010
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