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A query expansion framework in image retrieval domain based on local and global analysis

Authors:

G. R. ThomaAuthors Info & Claims

Information Processing and Management: an International Journal, Volume 47, Issue 5

Pages 676 - 691

https://doi.org/10.1016/j.ipm.2010.12.001

Published: 01 September 2011 Publication History

Abstract

We present an image retrieval framework based on automatic query expansion in a concept feature space by generalizing the vector space model of information retrieval. In this framework, images are represented by vectors of weighted concepts similar to the keyword-based representation used in text retrieval. To generate the concept vocabularies, a statistical model is built by utilizing Support Vector Machine (SVM)-based classification techniques. The images are represented as ''bag of concepts'' that comprise perceptually and/or semantically distinguishable color and texture patches from local image regions in a multi-dimensional feature space. To explore the correlation between the concepts and overcome the assumption of feature independence in this model, we propose query expansion techniques in the image domain from a new perspective based on both local and global analysis. For the local analysis, the correlations between the concepts based on the co-occurrence pattern, and the metrical constraints based on the neighborhood proximity between the concepts in encoded images, are analyzed by considering local feedback information. We also analyze the concept similarities in the collection as a whole in the form of a similarity thesaurus and propose an efficient query expansion based on the global analysis. The experimental results on a photographic collection of natural scenes and a biomedical database of different imaging modalities demonstrate the effectiveness of the proposed framework in terms of precision and recall.

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Cited By

Pellegrin LEscalante HMontes-Y-Gómez MGonzález F(2019)Exploiting label semantic relatedness for unsupervised image annotation with large free vocabulariesMultimedia Tools and Applications10.1007/s11042-019-7357-278:14(19641-19662)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.1007/s11042-019-7357-2
Houle MMa XOria VSun J(2017)Query Expansion for Content-Based Similarity Search Using Local and Global FeaturesACM Transactions on Multimedia Computing, Communications, and Applications10.1145/306359513:3(1-23)Online publication date: 31-May-2017
https://dl.acm.org/doi/10.1145/3063595
Wang XLiang LLi WLi DYang H(2016)A new SVM-based relevance feedback image retrieval using probabilistic feature and weighted kernel functionJournal of Visual Communication and Image Representation10.1016/j.jvcir.2016.03.00838:C(256-275)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1016/j.jvcir.2016.03.008
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A query expansion framework in image retrieval domain based on local and global analysis
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cover image Information Processing and Management: an International Journal

Information Processing and Management: an International Journal Volume 47, Issue 5

September, 2011

173 pages

ISSN:0306-4573

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Copyright © Elsevier Ltd © 2010.

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Pergamon Press, Inc.

United States

Publication History

Published: 01 September 2011

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Cited By

Pellegrin LEscalante HMontes-Y-Gómez MGonzález F(2019)Exploiting label semantic relatedness for unsupervised image annotation with large free vocabulariesMultimedia Tools and Applications10.1007/s11042-019-7357-278:14(19641-19662)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.1007/s11042-019-7357-2
Houle MMa XOria VSun J(2017)Query Expansion for Content-Based Similarity Search Using Local and Global FeaturesACM Transactions on Multimedia Computing, Communications, and Applications10.1145/306359513:3(1-23)Online publication date: 31-May-2017
https://dl.acm.org/doi/10.1145/3063595
Wang XLiang LLi WLi DYang H(2016)A new SVM-based relevance feedback image retrieval using probabilistic feature and weighted kernel functionJournal of Visual Communication and Image Representation10.1016/j.jvcir.2016.03.00838:C(256-275)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1016/j.jvcir.2016.03.008
Zarchi MMonadjemi AJamshidi K(2015)A concept-based model for image retrieval systemsComputers and Electrical Engineering10.1016/j.compeleceng.2015.06.01846:C(303-313)Online publication date: 1-Aug-2015
https://dl.acm.org/doi/10.1016/j.compeleceng.2015.06.018
Zarchi MMonadjemi AJamshidi K(2014)A semantic model for general purpose content-based image retrieval systemsComputers and Electrical Engineering10.1016/j.compeleceng.2014.07.00840:7(2062-2071)Online publication date: 1-Oct-2014
https://dl.acm.org/doi/10.1016/j.compeleceng.2014.07.008

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