Relevance Feedback For Image Retrieval Using Transfer Learning and Improved MQHOA
Image retrieval is a challenging technology in multimedia applications where meeting the users’ subjective retrieval needs while achieving high retrieval performance is insufficient for existing methods. In this work, a related feedback image retrieval algorithm based on deep learning and optimizati...
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| Vydané v: | Journal of physics. Conference series Ročník 1880; číslo 1; s. 12006 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
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Bristol
IOP Publishing
01.04.2021
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| ISSN: | 1742-6588, 1742-6596 |
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| Abstract | Image retrieval is a challenging technology in multimedia applications where meeting the users’ subjective retrieval needs while achieving high retrieval performance is insufficient for existing methods. In this work, a related feedback image retrieval algorithm based on deep learning and optimization algorithm (CAMQHOA-RF) is proposed. Transfer learning based on the deep convolutional neural network is applied to extract deeper image features to reduce the semantic gap. The multi-scale quantum harmonic oscillator algorithm improved by the idea of “aggregation” is introduced to search the feature space effectively. The covariance matrix is used to strengthen the relationship between feature points at different scales to guide feature points to approach ideal query points faster. Moreover, the query point is reselected based on the feedback information to explore more potential users’ interest areas. Experiments have shown that compared with other algorithms, the proposed algorithm has fewer parameters that need to be set, but higher retrieval accuracy, faster retrieval speed, and stronger robustness are obtained, which can meet users better. |
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| AbstractList | Image retrieval is a challenging technology in multimedia applications where meeting the users’ subjective retrieval needs while achieving high retrieval performance is insufficient for existing methods. In this work, a related feedback image retrieval algorithm based on deep learning and optimization algorithm (CAMQHOA-RF) is proposed. Transfer learning based on the deep convolutional neural network is applied to extract deeper image features to reduce the semantic gap. The multi-scale quantum harmonic oscillator algorithm improved by the idea of “aggregation” is introduced to search the feature space effectively. The covariance matrix is used to strengthen the relationship between feature points at different scales to guide feature points to approach ideal query points faster. Moreover, the query point is reselected based on the feedback information to explore more potential users’ interest areas. Experiments have shown that compared with other algorithms, the proposed algorithm has fewer parameters that need to be set, but higher retrieval accuracy, faster retrieval speed, and stronger robustness are obtained, which can meet users better. |
| Author | Wang, Huaqiu Liu, Qian |
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| Cites_doi | 10.1109/TMM.2010.2046269 10.1016/j.neucom.2013.08.007 10.1109/TPAMI.2017.2709749 10.1109/TGRS.2015.2478379 10.1016/j.neucom.2014.07.078 10.3390/e20080577 10.1016/j.asoc.2010.11.009 |
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| DOI | 10.1088/1742-6596/1880/1/012006 |
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| References | Li (JPCS_1880_1_012006bib6) 2013; 11 Babenko (JPCS_1880_1_012006bib1) 2014; 8689 Tzelepi (JPCS_1880_1_012006bib8) 2014; 127 Broilo (JPCS_1880_1_012006bib9) 2010; 12 Wang (JPCS_1880_1_012006bib5) 2011; 11 Moreno-Picot (JPCS_1880_1_012006bib10) 2011; 151 Zheng (JPCS_1880_1_012006bib3) 2018; 40 Lu (JPCS_1880_1_012006bib4) 2018; 20 Wang (JPCS_1880_1_012006bib7) 2014; 127 Su (JPCS_1880_1_012006bib12) 2018 Romero (JPCS_1880_1_012006bib2) 2016; 54 Kanimozhi (JPCS_1880_1_012006bib11) 2015; 11 |
| References_xml | – volume: 12 start-page: 267 year: 2010 ident: JPCS_1880_1_012006bib9 article-title: A stochastic approach to image retrieval using relevance feedback and particle swarm optimization publication-title: IEEE Transactions on Multimedia doi: 10.1109/TMM.2010.2046269 – volume: 127 start-page: 214 year: 2014 ident: JPCS_1880_1_012006bib7 article-title: An image retrieval scheme with relevance feedback using feature reconstruction and SVM reclassification publication-title: Neurocomputing doi: 10.1016/j.neucom.2013.08.007 – volume: 8689 start-page: 584 year: 2014 ident: JPCS_1880_1_012006bib1 – year: 2018 ident: JPCS_1880_1_012006bib12 article-title: Is Robustness the Cost of Accuracy? – volume: 40 start-page: 1224 year: 2018 ident: JPCS_1880_1_012006bib3 article-title: SIFT meets CNN:A decade survey of instance retrieval publication-title: IEEE transactions on pattern analysis and machine intelligence doi: 10.1109/TPAMI.2017.2709749 – volume: 54 start-page: 1349 year: 2016 ident: JPCS_1880_1_012006bib2 article-title: Unsupervised deep feature extraction for remote sensing image classification publication-title: IEEE Transactions on Geoscience and Remote Sensing doi: 10.1109/TGRS.2015.2478379 – volume: 127 start-page: 214 year: 2014 ident: JPCS_1880_1_012006bib8 article-title: Relevance Feedback in Deep Convolutional Neural Networks for Content Based Image Retrieval publication-title: Neurocomputing – volume: 11 start-page: 1099 year: 2015 ident: JPCS_1880_1_012006bib11 article-title: An integrated approach to region based image retrieval using firefly algorithm and support vector machine publication-title: Neurocomputing doi: 10.1016/j.neucom.2014.07.078 – volume: 20 start-page: 577 year: 2018 ident: JPCS_1880_1_012006bib4 article-title: An adaptive weight method for image retrieval based multi-feature fusion. Entropy publication-title: Entropy doi: 10.3390/e20080577 – volume: 151 start-page: 1782 year: 2011 ident: JPCS_1880_1_012006bib10 article-title: Distance-based relevance feedback using a hybrid interactive genetic algorithm for image retrieval publication-title: Applied Soft Computing – volume: 11 start-page: 2787 year: 2011 ident: JPCS_1880_1_012006bib5 article-title: A new integrated SVM classifiers for relevance feedback content-based image retrieval using EM. parameter estimation publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2010.11.009 – volume: 11 start-page: 3634 year: 2013 ident: JPCS_1880_1_012006bib6 article-title: Improving Relevance Feedback in Image Retrieval by Incorporating Unlabelled Images publication-title: Telkomnika Indonesian Journal of Electrical Engineering |
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| SubjectTerms | Algorithms Artificial neural networks Covariance matrix Feature extraction Feedback Harmonic oscillators Image management Image retrieval Machine learning Multimedia Optimization Physics |
| Title | Relevance Feedback For Image Retrieval Using Transfer Learning and Improved MQHOA |
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