article

Active SVM-based relevance feedback using multiple classifiers ensemble and features reweighting

Authors:

Xiang-Yang Wang,

Hong-Ying YangAuthors Info & Claims

Engineering Applications of Artificial Intelligence, Volume 26, Issue 1

Pages 368 - 381

https://doi.org/10.1016/j.engappai.2012.05.008

Published: 01 January 2013 Publication History

Abstract

Relevance feedback (RF) is an effective approach to bridge the gap between low-level visual features and high-level semantic meanings in content-based image retrieval (CBIR). The active support vector machine (SVM) based RFs have been popular because they can outperform many other classifiers when the size of the training set is small, but they are often very complex and some unsatisfactory relevance of results occur frequently. To overcome the above limitations, an active SVM-based RF using multiple classifiers ensemble and features reweighting is proposed in this paper. Firstly, we select the most informative images by using active learning method for user to label, and quickly learn a boundary that separates the images that satisfy the user's query concept from the rest of the dataset. Secondly, the feature space is modified dynamically by appropriately weighting the descriptive features according to a set of statistical characteristics. Then, a set of moderate accurate one-class SVM classifiers are trained separately by using different new sub-features vectors. Finally, we compute the weight vector of component SVM classifiers dynamically by using the parameters for positive and negative samples, and combine the results of the component classifiers to form an output code as a hypothesized solution to the overall image retrieval problem. Extensive simulations on large databases show that the proposed algorithm is significantly more effective than the state-of-the-art approaches.

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Cited By

Thusnavis Bella Mary I Bruntha PManimekalai MSagayam KDang H(2021)Investigation of an Efficient Integrated Semantic Interactive Algorithm for Image RetrievalPattern Recognition and Image Analysis10.1134/S105466182104023431:4(709-721)Online publication date: 1-Oct-2021
https://dl.acm.org/doi/10.1134/S1054661821040234
Kim BPardo B(2018)A Human-in-the-Loop System for Sound Event Detection and AnnotationACM Transactions on Interactive Intelligent Systems10.1145/32143668:2(1-23)Online publication date: 21-Jun-2018
https://dl.acm.org/doi/10.1145/3214366
Rao YLiu WFan BSong JYang Y(2018)A novel relevance feedback method for CBIRWorld Wide Web10.1007/s11280-017-0523-421:6(1505-1522)Online publication date: 1-Nov-2018
https://dl.acm.org/doi/10.1007/s11280-017-0523-4
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Index Terms

Active SVM-based relevance feedback using multiple classifiers ensemble and features reweighting
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

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cover image Engineering Applications of Artificial Intelligence

Engineering Applications of Artificial Intelligence Volume 26, Issue 1

January, 2013

668 pages

ISSN:0952-1976

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Copyright © Elsevier Ltd © 2012.

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Pergamon Press, Inc.

United States

Publication History

Published: 01 January 2013

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Cited By

Thusnavis Bella Mary I Bruntha PManimekalai MSagayam KDang H(2021)Investigation of an Efficient Integrated Semantic Interactive Algorithm for Image RetrievalPattern Recognition and Image Analysis10.1134/S105466182104023431:4(709-721)Online publication date: 1-Oct-2021
https://dl.acm.org/doi/10.1134/S1054661821040234
Kim BPardo B(2018)A Human-in-the-Loop System for Sound Event Detection and AnnotationACM Transactions on Interactive Intelligent Systems10.1145/32143668:2(1-23)Online publication date: 21-Jun-2018
https://dl.acm.org/doi/10.1145/3214366
Rao YLiu WFan BSong JYang Y(2018)A novel relevance feedback method for CBIRWorld Wide Web10.1007/s11280-017-0523-421:6(1505-1522)Online publication date: 1-Nov-2018
https://dl.acm.org/doi/10.1007/s11280-017-0523-4
Kim BPardo BPapadopoulos GKuflik TChen FDuarte CFu W(2017)I-SEDProceedings of the 22nd International Conference on Intelligent User Interfaces10.1145/3025171.3025231(553-557)Online publication date: 7-Mar-2017
https://dl.acm.org/doi/10.1145/3025171.3025231
Boteanu BMironicăź IIonescu B(2017)Pseudo-relevance feedback diversification of social image retrieval resultsMultimedia Tools and Applications10.1007/s11042-016-3678-676:9(11889-11916)Online publication date: 1-May-2017
https://dl.acm.org/doi/10.1007/s11042-016-3678-6
Qi YZhang G(2016)Strategy of active learning support vector machine for image retrievalIET Computer Vision10.1049/iet-cvi.2015.010110:1(87-94)Online publication date: 1-Feb-2016
https://dl.acm.org/doi/10.1049/iet-cvi.2015.0101
Kausar NMajid AJaved S(2016)Developing learning based intelligent fusion for deblurring confocal microscopic imagesEngineering Applications of Artificial Intelligence10.1016/j.engappai.2016.08.00655:C(339-352)Online publication date: 1-Oct-2016
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Chang BTsai HYen C(2016)SVM-PSO based rotation-invariant image texture classification in SVD and DWT domainsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2016.02.00552:C(96-107)Online publication date: 1-Jun-2016
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Mironică IIonescu BUijlings JSebe N(2016)Fisher Kernel Temporal Variation-based Relevance Feedback for video retrievalComputer Vision and Image Understanding10.1016/j.cviu.2015.10.005143:C(38-51)Online publication date: 1-Feb-2016
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James SCollomosse JHall PCollomosse JCosker D(2014)Interactive video asset retrieval using sketched queriesProceedings of the 11th European Conference on Visual Media Production10.1145/2668904.2668940(1-8)Online publication date: 13-Nov-2014
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