research-article

Graph-based multiple-instance learning for object-based image retrieval

Authors:

Hong-Jiang ZhangAuthors Info & Claims

MIR '08: Proceedings of the 1st ACM international conference on Multimedia information retrieval

Pages 156 - 163

https://doi.org/10.1145/1460096.1460123

Published: 30 October 2008 Publication History

Abstract

We study in this paper the problem of using multiple-instance semi-supervised learning to solve object-based image retrieval problem, in which the user is only interested in a portion of the image, and the rest of the image is considered as irrelevant. Although many multiple-instance learning (MIL) algorithms have been proposed to solve object-based image retrieval problem, most of them only have a supervised manner and do not fully utilize the information of the unlabeled data in the image collection. In this paper, to make use of the large amount of unlabeled data, we present a semi-supervised version of multiple-instance learning, i.e. multiple-instance semi-supervised learning (MISSL). By taking into account both the multiple-instance property and the semi-supervised property simultaneously, a novel regularization framework for MISSL is presented. Based on this framework, a graph-based multiple-instance learning (GMIL) algorithm is developed, in which three kinds of data, i.e. labeled data, semi-labeled data, and unlabeled data simultaneously propagate information on a graph. Moreover, under the same framework, GMIL can be reduced to a novel standard MIL algorithm (GMIL-M) by ignoring unlabeled data. We theoretically prove the convergence of the iterative solutions for GMIL and GMIL-M. We apply GMIL algorithm to solving object-based image retrieval problem, and experimental results show the superiority of the proposed method. Some experiments on standard MIL problems are also provided to show the competitiveness of the proposed algorithms compared with state-of-the-art MIL algorithms.

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Cited By

Tian DZhang Y(2022)Multi-instance Learning for Semantic Image AnalysisIntelligent Information Processing XI10.1007/978-3-031-03948-5_38(473-484)Online publication date: 25-Apr-2022
https://doi.org/10.1007/978-3-031-03948-5_38
Xu TGondra IChiu D(2017)A maximum partial entropy-based method for multiple-instance concept learningApplied Intelligence10.1007/s10489-016-0873-046:4(865-875)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1007/s10489-016-0873-0
Li WLiu CKang SLi JTseng YZhou X(2016)Pushing the annotation of cellular activities to a higher resolution: Predicting functions at the isoform levelMethods10.1016/j.ymeth.2015.07.01693(110-118)Online publication date: Jan-2016
https://doi.org/10.1016/j.ymeth.2015.07.016
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Index Terms

Graph-based multiple-instance learning for object-based image retrieval
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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Published In

cover image ACM Conferences

MIR '08: Proceedings of the 1st ACM international conference on Multimedia information retrieval

October 2008

506 pages

ISBN:9781605583129

DOI:10.1145/1460096

General Chair:
Michael S. Lew
Leiden University, The Netherlands
,
Program Chairs:
Alberto del Bimbo
University of Florence, Italy
,
Erwin M. Bakker
Leiden University, The Netherlands

Copyright © 2008 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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Publication History

Published: 30 October 2008

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MM08: ACM Multimedia Conference 2008

October 30 - 31, 2008

British Columbia, Vancouver, Canada

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The 32nd ACM International Conference on Multimedia

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Citations

Cited By

Tian DZhang Y(2022)Multi-instance Learning for Semantic Image AnalysisIntelligent Information Processing XI10.1007/978-3-031-03948-5_38(473-484)Online publication date: 25-Apr-2022
https://doi.org/10.1007/978-3-031-03948-5_38
Xu TGondra IChiu D(2017)A maximum partial entropy-based method for multiple-instance concept learningApplied Intelligence10.1007/s10489-016-0873-046:4(865-875)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.1007/s10489-016-0873-0
Li WLiu CKang SLi JTseng YZhou X(2016)Pushing the annotation of cellular activities to a higher resolution: Predicting functions at the isoform levelMethods10.1016/j.ymeth.2015.07.01693(110-118)Online publication date: Jan-2016
https://doi.org/10.1016/j.ymeth.2015.07.016
Liu CChen TDing XZou HTong Y(2016)A multi-instance multi-label learning algorithm based on instance correlationsMultimedia Tools and Applications10.1007/s11042-016-3494-z75:19(12263-12284)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1007/s11042-016-3494-z
Ting-Chu Lin Min-Chun Yang Chia-Yin Tsai Wang Y(2015)Query-Adaptive Multiple Instance Learning for Video Instance RetrievalIEEE Transactions on Image Processing10.1109/TIP.2015.240323624:4(1330-1340)Online publication date: Apr-2015
https://doi.org/10.1109/TIP.2015.2403236
Li FLiu R(2015)Multi-graph multi-instance learning with soft label consistency for object-based image retrieval2015 IEEE International Conference on Multimedia and Expo (ICME)10.1109/ICME.2015.7177391(1-6)Online publication date: Jun-2015
https://doi.org/10.1109/ICME.2015.7177391
Li DLi NWang JZhu T(2015)Pornographic images recognition based on spatial pyramid partition and multi-instance ensemble learningKnowledge-Based Systems10.1016/j.knosys.2015.04.01484:C(214-223)Online publication date: 1-Aug-2015
https://dl.acm.org/doi/10.1016/j.knosys.2015.04.014
Chen TDeng H(2013)Multi-Instance Learning for Image Retrieval with Relevance FeedbackApplied Mechanics and Materials10.4028/www.scientific.net/AMM.427-429.1606427-429(1606-1609)Online publication date: Sep-2013
https://doi.org/10.4028/www.scientific.net/AMM.427-429.1606
Li WKang SLiu CZhang SShi YLiu YZhou X(2013)High-resolution functional annotation of human transcriptome: predicting isoform functions by a novel multiple instance-based label propagation methodNucleic Acids Research10.1093/nar/gkt136242:6(e39-e39)Online publication date: 25-Dec-2013
https://doi.org/10.1093/nar/gkt1362
Zhuang LGao HLuo JLin Z(2013)Regularized Semi-Supervised Latent Dirichlet Allocation for visual concept learningNeurocomputing10.1016/j.neucom.2012.04.043119(26-32)Online publication date: 1-Nov-2013
https://dl.acm.org/doi/10.1016/j.neucom.2012.04.043
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