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Video-based image retrieval

Published: 28 November 2011 Publication History

Abstract

Likely variations in the capture conditions (e.g. light, blur, scale, occlusion) and in the viewpoint between the query image and the images in the collection are the factors due to which image retrieval based on the Query-by-Example (QBE) principle is still not reliable enough. In this paper, we propose a novel QBE-based image retrieval system where users are allowed to submit a short video clip as a query to improve the retrieval reliability. Improvement is achieved by integrating the information about different viewpoints and conditions under which object and scene appearances can be captured across different video frames. Rich information extracted from a video can be exploited to generate a more complete query representation than in the case of a single-image query and to improve the relevance of the retrieved results. Our experimental results show that video-based image retrieval (VBIR) is significantly more reliable than the retrieval using a single image as a query.

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D. G. Lowe. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision, 60(2):91--110, 2004.
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J. Sivic, F. Schaffalitzky, and A. Zisserman. Object level grouping for video shots. 2004.
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Cited By

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  • (2015)Semantic Similarity Based Video Reranking2015 International Conference on Computational Intelligence and Communication Networks (CICN)10.1109/CICN.2015.274(1420-1423)Online publication date: Dec-2015
  • (2014)Frame feature tracking for speed estimation2014 14th International Conference on Advances in ICT for Emerging Regions (ICTer)10.1109/ICTER.2014.7083875(29-34)Online publication date: Dec-2014
  • (2013)Mobile image retrieval using multi-photos as query2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)10.1109/ICMEW.2013.6618255(1-4)Online publication date: Jul-2013
  • Show More Cited By

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  1. Video-based image retrieval

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    cover image ACM Conferences
    MM '11: Proceedings of the 19th ACM international conference on Multimedia
    November 2011
    944 pages
    ISBN:9781450306164
    DOI:10.1145/2072298
    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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    New York, NY, United States

    Publication History

    Published: 28 November 2011

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    Author Tags

    1. content-based image retrieval
    2. video-based image retrieval

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    MM '11
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    MM '11: ACM Multimedia Conference
    November 28 - December 1, 2011
    Arizona, Scottsdale, USA

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    Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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    Cited By

    View all
    • (2015)Semantic Similarity Based Video Reranking2015 International Conference on Computational Intelligence and Communication Networks (CICN)10.1109/CICN.2015.274(1420-1423)Online publication date: Dec-2015
    • (2014)Frame feature tracking for speed estimation2014 14th International Conference on Advances in ICT for Emerging Regions (ICTer)10.1109/ICTER.2014.7083875(29-34)Online publication date: Dec-2014
    • (2013)Mobile image retrieval using multi-photos as query2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)10.1109/ICMEW.2013.6618255(1-4)Online publication date: Jul-2013
    • (2012)Constrained keypoint quantizationProceedings of the 2nd ACM International Conference on Multimedia Retrieval10.1145/2324796.2324816(1-8)Online publication date: 5-Jun-2012

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