tutorial

Superimage: Packing Semantic-Relevant Images for Indexing and Retrieval

Authors:

Shiliang Zhang,

Qi TianAuthors Info & Claims

ICMR '14: Proceedings of International Conference on Multimedia Retrieval

Pages 41 - 48

https://doi.org/10.1145/2578726.2578741

Published: 01 April 2014 Publication History

Abstract

As an important procedure in image retrieval, off-line indexing focuses on organizing relevant images together and making them easy to access. However, most of existing indexing strategies view database images individually and only consider partial relevance, i.e., either visual or semantic relevance among them. To overcome these issues and design better indexing strategy, we propose to package semantically relevant images into superimages, and then index superimages instead of single images. Superimage effectively packages multiple images into one new unit, hence significantly decreases the number of images to be indexed. This naturally saves the memory cost and retrieval time. To make the final index file discriminative to both visual and semantic relevances, we extract local descriptors from superimages and index them with inverted file. During online retrieval, we only need to extract local descriptors from queries, but could get semantic-aware retrieval results. This is because during our off-line indexing stage, both the semantically and visually relevant images are organized together. Therefore, our approach is superior to many online retrieval fusion algorithms. Experimental results on UKbench, Holidays, and one large-scale dataset all manifest the promising performance of our approach, i.e., competitive precision, better efficiency, and only about 1/2 memory consumption compared with state-of-the-arts.

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Cited By

Markatopoulou FMezaris VPatras I(2019)Implicit and Explicit Concept Relations in Deep Neural Networks for Multi-Label Video/Image AnnotationIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2018.284845829:6(1631-1644)Online publication date: Jun-2019
https://doi.org/10.1109/TCSVT.2018.2848458
Markatopoulou FZampoglou MApostolidis EPapadopoulos SMezaris VPatras IKompatsiaris I(2019)Finding Semantically Related Videos in Closed CollectionsVideo Verification in the Fake News Era10.1007/978-3-030-26752-0_5(127-159)Online publication date: 18-Sep-2019
https://doi.org/10.1007/978-3-030-26752-0_5
Gil DRamos-Terrades OMinchole ESanchez Cde Frutos NDiez-Ferrer MOrtiz RRosell A(2017)Classification of Confocal Endomicroscopy Patterns for Diagnosis of Lung CancerComputer Assisted and Robotic Endoscopy and Clinical Image-Based Procedures10.1007/978-3-319-67543-5_15(151-159)Online publication date: 8-Sep-2017
https://doi.org/10.1007/978-3-319-67543-5_15
Show More Cited By

Index Terms

Superimage: Packing Semantic-Relevant Images for Indexing and Retrieval
1. Information systems
  1. Information retrieval
    1. Document representation

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Published In

cover image ACM Other conferences

ICMR '14: Proceedings of International Conference on Multimedia Retrieval

April 2014

564 pages

ISBN:9781450327824

DOI:10.1145/2578726

Conference Chairs:
Mohan Kankanhalli
National University of Singapore
,
Stefan Rueger
The Open University, UK
,
R. Manmatha
A9.com, USA
,
General Chairs:
Joemon Jose
University of Glasgow, UK
,
Keith van Rijsbergen
University of Glasgow, UK

Copyright © 2014 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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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 April 2014

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ICMR '14

ICMR '14: International Conference on Multimedia Retrieval

April 1 - 4, 2014

Glasgow, United Kingdom

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ICMR '14 Paper Acceptance Rate 21 of 111 submissions, 19%;

Overall Acceptance Rate 254 of 830 submissions, 31%

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Cited By

Markatopoulou FMezaris VPatras I(2019)Implicit and Explicit Concept Relations in Deep Neural Networks for Multi-Label Video/Image AnnotationIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2018.284845829:6(1631-1644)Online publication date: Jun-2019
https://doi.org/10.1109/TCSVT.2018.2848458
Markatopoulou FZampoglou MApostolidis EPapadopoulos SMezaris VPatras IKompatsiaris I(2019)Finding Semantically Related Videos in Closed CollectionsVideo Verification in the Fake News Era10.1007/978-3-030-26752-0_5(127-159)Online publication date: 18-Sep-2019
https://doi.org/10.1007/978-3-030-26752-0_5
Gil DRamos-Terrades OMinchole ESanchez Cde Frutos NDiez-Ferrer MOrtiz RRosell A(2017)Classification of Confocal Endomicroscopy Patterns for Diagnosis of Lung CancerComputer Assisted and Robotic Endoscopy and Clinical Image-Based Procedures10.1007/978-3-319-67543-5_15(151-159)Online publication date: 8-Sep-2017
https://doi.org/10.1007/978-3-319-67543-5_15
Xu ZDu JYe LFan D(2016)Multi-feature indexing for image retrieval based on hypergraph2016 4th International Conference on Cloud Computing and Intelligence Systems (CCIS)10.1109/CCIS.2016.7790309(494-500)Online publication date: Aug-2016
https://doi.org/10.1109/CCIS.2016.7790309
Pedronette DTorres R(2016)A correlation graph approach for unsupervised manifold learning in image retrieval tasksNeurocomputing10.1016/j.neucom.2016.03.081208:C(66-79)Online publication date: 5-Oct-2016
https://dl.acm.org/doi/10.1016/j.neucom.2016.03.081
Shiliang Zhang Xiaoyu Wang Yuanqing Lin Qi Tian (2015)Cross Indexing With GroupletsIEEE Transactions on Multimedia10.1109/TMM.2015.247805517:11(1969-1979)Online publication date: 1-Nov-2015
https://dl.acm.org/doi/10.1109/TMM.2015.2478055
Luo QZhang SHuang TGao WTian Q(2015)Hybrid-Indexing Multi-type Features for Large-Scale Image SearchComputer Vision – ACCV 201410.1007/978-3-319-16865-4_29(446-460)Online publication date: 16-Apr-2015
https://doi.org/10.1007/978-3-319-16865-4_29

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