poster

Asymmetric semi-supervised boosting for SVM active learning in CBIR

Authors:

Ming-Yu LuAuthors Info & Claims

CIVR '10: Proceedings of the ACM International Conference on Image and Video Retrieval

Pages 182 - 188

https://doi.org/10.1145/1816041.1816070

Published: 05 July 2010 Publication History

Abstract

Support vector machine (SVM) based active learning technique has played a key role to alleviate the burden of labeling in relevance feedback. However, most SVM-based active learning algorithms are challenged by the small example problem and the asymmetric distribution problem. This paper proposes a novel scheme that combines semi-supervised learning, ensemble learning and active learning in a uniform framework. Concretely, unlabeled data is exploited to facilitate ensemble learning by helping augment the diversity among the base SVM classifiers, and then the learned SVM ensemble model is used to identify the most informative examples for active learning. In particular, a bias-weighting mechanism is developed to guide the ensemble model to pay more attention on the positive examples than the negative ones. An empirical study shows that the proposed scheme is significantly more effective than some existing approaches.

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Cited By

Wang XZhang BYang H(2018)Active SVM-based relevance feedback using multiple classifiers ensemble and features reweightingEngineering Applications of Artificial Intelligence10.1016/j.engappai.2012.05.00826:1(368-381)Online publication date: 27-Dec-2018
https://dl.acm.org/doi/10.1016/j.engappai.2012.05.008
Wu JYuan JLuo J(2016)Robust Multi-view Manifold Ranking for Image RetrievalProceedings, Part II, of the 20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining - Volume 965210.1007/978-3-319-31750-2_8(92-103)Online publication date: 19-Apr-2016
https://dl.acm.org/doi/10.1007/978-3-319-31750-2_8
Wu DWu JLu MWang C(2014)A Two-Step Similarity Ranking Scheme for Image Retrieval2014 Sixth International Symposium on Parallel Architectures, Algorithms and Programming10.1109/PAAP.2014.26(191-196)Online publication date: Jul-2014
https://doi.org/10.1109/PAAP.2014.26
Show More Cited By

Index Terms

Asymmetric semi-supervised boosting for SVM active learning in CBIR
1. Information systems
  1. Information retrieval

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Published In

cover image ACM Conferences

CIVR '10: Proceedings of the ACM International Conference on Image and Video Retrieval

July 2010

492 pages

ISBN:9781450301176

DOI:10.1145/1816041

Conference Chairs:
Shipeng Li
Microsoft Research Asia, China
,
Xinbo Gao
Xidian University, China
,
Nicu Sebe
University of Trento, Italy

Copyright © 2010 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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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 July 2010

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National Natural Science Foundation of China

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CIVR' 10

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SIGMM

CIVR' 10: International Conference on Image and Video Retrieval

July 5 - 7, 2010

Xi'an, China

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Cited By

Wang XZhang BYang H(2018)Active SVM-based relevance feedback using multiple classifiers ensemble and features reweightingEngineering Applications of Artificial Intelligence10.1016/j.engappai.2012.05.00826:1(368-381)Online publication date: 27-Dec-2018
https://dl.acm.org/doi/10.1016/j.engappai.2012.05.008
Wu JYuan JLuo J(2016)Robust Multi-view Manifold Ranking for Image RetrievalProceedings, Part II, of the 20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining - Volume 965210.1007/978-3-319-31750-2_8(92-103)Online publication date: 19-Apr-2016
https://dl.acm.org/doi/10.1007/978-3-319-31750-2_8
Wu DWu JLu MWang C(2014)A Two-Step Similarity Ranking Scheme for Image Retrieval2014 Sixth International Symposium on Parallel Architectures, Algorithms and Programming10.1109/PAAP.2014.26(191-196)Online publication date: Jul-2014
https://doi.org/10.1109/PAAP.2014.26
Wu JXiao ZWang HShen H(2014)Learning with both unlabeled data and query logs for image searchComputers & Electrical Engineering10.1016/j.compeleceng.2013.09.00440:3(964-973)Online publication date: Apr-2014
https://doi.org/10.1016/j.compeleceng.2013.09.004
Wu JLi YFeng SShen H(2013)A Self-immunizing Manifold Ranking for Image RetrievalAdvances in Knowledge Discovery and Data Mining10.1007/978-3-642-37456-2_36(426-436)Online publication date: 2013
https://doi.org/10.1007/978-3-642-37456-2_36
Wu JLi YSang YShen H(2012)Incorporating Manifold Ranking with Active Learning in Relevance Feedback for Image RetrievalProceedings of the 2012 13th International Conference on Parallel and Distributed Computing, Applications and Technologies10.1109/PDCAT.2012.82(739-744)Online publication date: 14-Dec-2012
https://dl.acm.org/doi/10.1109/PDCAT.2012.82

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