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Article

Scalable integrated region-based image retrieval using IRM and statistical clustering

Authors:

Yanping DuAuthors Info & Claims

JCDL '01: Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries

Pages 268 - 277

https://doi.org/10.1145/379437.379679

Published: 01 January 2001 Publication History

Abstract

Statistical clustering is critical in designing scalable image retriev al systems. In this paper, we present a scalable algorithm for indexing and retrieving images based on region segmentation. The method uses statistical clustering on region features and IRM (Integrated Region Matching), a measure developed to evaluate overall similarity between images that incorporates properties of all the regions in the images by a region-matching scheme. Compared with retrieval based on individual regions, our overall similarity approach (a) reduces the influence of inaccurate segmentation, (b) helps to clarify the semantics of a particular region, and (c) enables a simple querying interface for region-based image retrieval systems. The algorithm has been implemented as a part of our experimental SIMPLIcity image retrieval system and tested on large-scale image databases of both general-purpose images and pathology slides. Experiments have demonstrated that this technique maintains the accuracy and robustness of the original system while reducing the matching time significantly.

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cover image ACM Conferences

JCDL '01: Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries

January 2001

481 pages

ISBN:1581133456

DOI:10.1145/379437

Chairmen:
Edward A. Fox
Virginia Tech
,
Christine L. Borgman
UCLA

Copyright © 2001 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 January 2001

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JCDL '01 Paper Acceptance Rate 76 of 250 submissions, 30%;

Overall Acceptance Rate 415 of 1,482 submissions, 28%

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Cited By

Orazayeva ATussupov JWójcik WPavlov SAbdikerimova GSavytska L(2022)METHODS FOR DETECTING AND SELECTING AREAS ON TEXTURE BIOMEDICAL IMAGES OF BREAST CANCERInformatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska10.35784/iapgos.295112:2(69-72)Online publication date: 30-Jun-2022
https://doi.org/10.35784/iapgos.2951
Sadique MKumar Biswas BRafizul Haque S(2019)Unsupervised Content-Based Image Retrieval Technique Using Global and Local Features2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT)10.1109/ICASERT.2019.8934595(1-6)Online publication date: May-2019
https://doi.org/10.1109/ICASERT.2019.8934595
Seetharaman KChembian W(2017)Statistical Distribution-based Color Image RetrivalProceedings of the 1st International Conference on Graphics and Signal Processing10.1145/3121360.3121372(6-9)Online publication date: 24-Jun-2017
https://dl.acm.org/doi/10.1145/3121360.3121372
Seetharaman KSathiyaprasad B(2017)Colour image retrieval based on mean vector and covariance tests2017 International Conference on Intelligent Sustainable Systems (ICISS)10.1109/ISS1.2017.8389243(611-616)Online publication date: Dec-2017
https://doi.org/10.1109/ISS1.2017.8389243
Seetharaman KSelvaraj S(2016)Statistical Tests of Hypothesis Based Color Image RetrievalJournal of Data Analysis and Information Processing10.4236/jdaip.2016.4200804:02(90-99)Online publication date: 2016
https://doi.org/10.4236/jdaip.2016.42008
Huang SWang W(2015)Retrieving images combining saliency detection with IRM2015 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP.2015.7350852(517-521)Online publication date: Sep-2015
https://doi.org/10.1109/ICIP.2015.7350852
Shriwas MRaut V(2015)Content based image retrieval: A past, present and new feature descriptor2015 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2015]10.1109/ICCPCT.2015.7159404(1-7)Online publication date: Mar-2015
https://doi.org/10.1109/ICCPCT.2015.7159404
Yang XLv FCai LLi D(2015)Adaptive learning region importance for region‐based image retrievalIET Computer Vision10.1049/iet-cvi.2014.01199:3(368-377)Online publication date: 1-Jun-2015
https://dl.acm.org/doi/10.1049/iet-cvi.2014.0119
Belim SKutlunin P(2014)Usage of Clustering Algorithm to Segment Image into Simply Connected DomainsScience and Education of the Bauman MSTU10.7463/0315.075927515:03Online publication date: 3-Dec-2014
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Zhan Q(2014)The Initial Retrieval Based on Image SegmentationAdvanced Materials Research10.4028/www.scientific.net/AMR.919-921.2131919-921(2131-2134)Online publication date: Apr-2014
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