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research-article

Separable vocabulary and feature fusion for image retrieval based on sparse representation

Authors:

Viacheslav Voronin,

Hengyou WangAuthors Info & Claims

Neurocomputing, Volume 236, Issue C

Pages 14 - 22

https://doi.org/10.1016/j.neucom.2016.08.106

Published: 02 May 2017 Publication History

Abstract

Visual vocabulary is the core of the Bag-of-visual-words (BOW) model in image retrieval. In order to ensure the retrieval accuracy, a large vocabulary is always used in traditional methods. However, a large vocabulary will lead to a low recall. In order to improve recall, vocabularies with medium sizes are proposed, but they will lead to a low accuracy. To address these two problems, we propose a new method for image retrieval based on feature fusion and sparse representation over separable vocabulary. Firstly, a large vocabulary is generated on the training dataset. Secondly, the vocabulary is separated into a number of vocabularies with medium sizes. Thirdly, for a given query image, we adopt sparse representation to select a vocabulary for retrieval. In the proposed method, the large vocabulary can guarantee a relatively high accuracy, while the vocabularies with medium sizes are responsible for high recall. Also, in order to reduce quantization error and improve recall, sparse representation scheme is used for visual words quantization. Moreover, both the local features and the global features are fused to improve the recall. Our proposed method is evaluated on two benchmark datasets, i.e., Coil20 and Holidays. Experiments show that our proposed method achieves good performance.

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Cited By

Sarwar AMehmood ZSaba TQazi KAdnan AJamal H(2020)A novel method for content-based image retrieval to improve the effectiveness of the bag-of-words model using a support vector machineJournal of Information Science10.1177/016555151878282545:1(117-135)Online publication date: 18-Jun-2020
https://dl.acm.org/doi/10.1177/0165551518782825
Venkatasalam KRjendran PThangavel M(2019)RETRACTED ARTICLE: Fuzzy rough subset method with region based mining to improve the retrieval and ranking of real time images over larger image databaseMultimedia Tools and Applications10.1007/s11042-019-7289-x79:5-6(3861-3878)Online publication date: 20-Feb-2019
https://dl.acm.org/doi/10.1007/s11042-019-7289-x
Dubey S(2019)Face retrieval using frequency decoded local descriptorMultimedia Tools and Applications10.1007/s11042-018-7028-878:12(16411-16431)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s11042-018-7028-8
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Information & Contributors

Information

Published In

cover image Neurocomputing

Neurocomputing Volume 236, Issue C

May 2017

160 pages

ISSN:0925-2312

Issue’s Table of Contents

Copyright © Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 02 May 2017

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Cited By

Sarwar AMehmood ZSaba TQazi KAdnan AJamal H(2020)A novel method for content-based image retrieval to improve the effectiveness of the bag-of-words model using a support vector machineJournal of Information Science10.1177/016555151878282545:1(117-135)Online publication date: 18-Jun-2020
https://dl.acm.org/doi/10.1177/0165551518782825
Venkatasalam KRjendran PThangavel M(2019)RETRACTED ARTICLE: Fuzzy rough subset method with region based mining to improve the retrieval and ranking of real time images over larger image databaseMultimedia Tools and Applications10.1007/s11042-019-7289-x79:5-6(3861-3878)Online publication date: 20-Feb-2019
https://dl.acm.org/doi/10.1007/s11042-019-7289-x
Dubey S(2019)Face retrieval using frequency decoded local descriptorMultimedia Tools and Applications10.1007/s11042-018-7028-878:12(16411-16431)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1007/s11042-018-7028-8
Kan SCen YCen YWang YVoronin VMladenovic VZeng M(2017)SURF binarization and fast codebook construction for image retrievalJournal of Visual Communication and Image Representation10.1016/j.jvcir.2017.08.00649:C(104-114)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1016/j.jvcir.2017.08.006

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