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Laplacian optimal design for image retrieval

Published: 23 July 2007 Publication History

Abstract

Relevance feedback is a powerful technique to enhance Content-Based Image Retrieval (CBIR) performance. It solicits the user's relevance judgments on the retrieved images returned by the CBIR systems. The user's labeling is then used to learn a classifier to distinguish between relevant and irrelevant images. However, the top returnedimages may not be the most informative ones. The challenge is thus to determine which unlabeled images would be the most informative (i.e., improve the classifier the most) if they were labeled and used as training samples. In this paper, we propose a novel active learning algorithm, called Laplacian Optimal Design (LOD), for relevance feedback image retrieval. Our algorithm is based on aregression model which minimizes the least square error on the measured (or, labeled) images and simultaneously preserves the local geometrical structure of the image space. Specifically, we assume that if two images are sufficiently close to each other, then their measurements (or, labels) are close as well. By constructing a nearest neighbor graph, the geometrical structure of the image space can be described by the graph Laplacian. We discuss how results from the field of optimal experimental design may be used to guide our selection of a subset of images, which gives us the most amount of information. Experimental results on Corel database suggest that theproposed approach achieves higher precision in relevance feedback image retrieval.

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  • (2022)A Laplacian‐regularized dual‐phase Gaussian process technique for semi‐supervised response surface modeling of black‐box functionsQuality and Reliability Engineering International10.1002/qre.318738:8(4073-4098)Online publication date: 29-Aug-2022
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cover image ACM Conferences
SIGIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
July 2007
946 pages
ISBN:9781595935977
DOI:10.1145/1277741
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: 23 July 2007

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Author Tags

  1. active learning
  2. experimental design
  3. image retrieval
  4. regression
  5. relevance feedback

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SIGIR07
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SIGIR07: The 30th Annual International SIGIR Conference
July 23 - 27, 2007
Amsterdam, The Netherlands

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

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  • (2023)Active ordinal classification by querying relative informationIntelligent Data Analysis10.3233/IDA-22689927:4(977-1002)Online publication date: 20-Jul-2023
  • (2023)Confidence-Aware Active Feedback for Interactive Instance SearchIEEE Transactions on Multimedia10.1109/TMM.2022.321796525(7173-7184)Online publication date: 1-Jan-2023
  • (2022)A Laplacian‐regularized dual‐phase Gaussian process technique for semi‐supervised response surface modeling of black‐box functionsQuality and Reliability Engineering International10.1002/qre.318738:8(4073-4098)Online publication date: 29-Aug-2022
  • (2020)An Active Learning Methodology for Efficient Estimation of Expensive Noisy Black-Box Functions Using Gaussian Process RegressionIEEE Access10.1109/ACCESS.2020.30028198(111460-111474)Online publication date: 2020
  • (2020)A Novel Active Learning Algorithm for Robust Image ClassificationIEEE Access10.1109/ACCESS.2020.29680828(71106-71116)Online publication date: 2020
  • (2019)Recursive Maximum Margin Active LearningIEEE Access10.1109/ACCESS.2019.29153347(59933-59943)Online publication date: 2019
  • (2019)Sequential Laplacian regularized V-optimal design of experiments for response surface modeling of expensive tests: An application in wind tunnel testingIISE Transactions10.1080/24725854.2018.150892851:5(559-576)Online publication date: 19-Feb-2019
  • (2017)Vicus: Exploiting local structures to improve network-based analysis of biological dataPLOS Computational Biology10.1371/journal.pcbi.100562113:10(e1005621)Online publication date: 12-Oct-2017
  • (2016)Image Classification by Cross-Media Active Learning With Privileged InformationIEEE Transactions on Multimedia10.1109/TMM.2016.260293818:12(2494-2502)Online publication date: 1-Dec-2016
  • (2016)Online Discriminative Tracking With Active Example SelectionIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2015.239579126:7(1279-1292)Online publication date: Jul-2016
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