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Incorporating camera metadata for attended region detection and consumer photo classification

Published: 19 October 2009 Publication History

Abstract

Photos taken by human beings significantly differ from the pictures that are taken by a surveillance camera or a vision sensor on a robot, e.g., human beings may intentionally capture photos to express his/her feeling or record a memorial scene. Such a creative photo capture process is accomplished by adjusting two factors: (1) the parameters setting of a camera; and (2) the position between the camera and the interesting objects or scenes. To enable automatic understanding and interpretation of the semantics of photos, it is very important to take all these factors into account. Unfortunately, most existing algorithms for image understanding focus on only the content of the images while completely ignoring these two important factors. In this paper, we have developed a new algorithm to calculate what the interestingness of the photographer is and what the core content of a photo is. The gained information (i.e., attended regions and attention of the photographer) is further used to support more effective photo classification and retrieval. Our experiments on 70,000+ photos taken by 200+ different models of cameras have obtained very positive results.

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Cited By

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  • (2019)Improving Saliency Detection Based on Modeling Photographer's IntentionIEEE Transactions on Multimedia10.1109/TMM.2018.285138921:1(124-134)Online publication date: Jan-2019
  • (2014)Personal Media ReunionProceedings of the 20th Anniversary International Conference on MultiMedia Modeling - Volume 832510.1007/978-3-319-04114-8_16(183-194)Online publication date: 6-Jan-2014
  • (2013)A survey on automatic techniques for enhancement and analysis of digital photographyJournal of the Brazilian Computer Society10.1007/s13173-013-0102-119:3(341-359)Online publication date: 26-Mar-2013
  • Show More Cited By

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  1. Incorporating camera metadata for attended region detection and consumer photo classification

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      cover image ACM Conferences
      MM '09: Proceedings of the 17th ACM international conference on Multimedia
      October 2009
      1202 pages
      ISBN:9781605586083
      DOI:10.1145/1631272
      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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      Publication History

      Published: 19 October 2009

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      Author Tags

      1. attended regions
      2. camera metadata
      3. image classification

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      MM09
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      MM09: ACM Multimedia Conference
      October 19 - 24, 2009
      Beijing, China

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      Cited By

      View all
      • (2019)Improving Saliency Detection Based on Modeling Photographer's IntentionIEEE Transactions on Multimedia10.1109/TMM.2018.285138921:1(124-134)Online publication date: Jan-2019
      • (2014)Personal Media ReunionProceedings of the 20th Anniversary International Conference on MultiMedia Modeling - Volume 832510.1007/978-3-319-04114-8_16(183-194)Online publication date: 6-Jan-2014
      • (2013)A survey on automatic techniques for enhancement and analysis of digital photographyJournal of the Brazilian Computer Society10.1007/s13173-013-0102-119:3(341-359)Online publication date: 26-Mar-2013
      • (2011)Capturing a great photo via learning from community-contributed photo collectionsProceedings of the 19th ACM international conference on Multimedia10.1145/2072298.2072470(809-810)Online publication date: 28-Nov-2011
      • (2011)Bilinear deep learning for image classificationProceedings of the 19th ACM international conference on Multimedia10.1145/2072298.2072344(343-352)Online publication date: 28-Nov-2011

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