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Attention region selection with information from professional digital camera

Published: 06 November 2005 Publication History

Abstract

The attentive region extraction is a challenging issue for semantic interpretation of image and video content. The successful attentive region extraction greatly facilitates image classification, adaptation, compression and retrieval. Different from the traditional visual attention detection models, we propose a new attentive region extraction method based on out-of-focus blurring (OFB) technique used by professional photographers. Firstly, we combine metadata in Exchangeable Image File Format (EXIF) with visual features to quickly select professional photographs from image database. After that, an algorithm is implemented to automatically extract the attentive region from these photographs. This algorithm measures the saliency for individual pixels based on edge distribution of the images. The experimental results on OFB images have proved that our approach is able to overcome the contrast map selection problem of traditional visual attention methods and extract the attentive region using OFB information. The attentive region generated by our algorithm has similar shape and size with the subject of photographs which is a useful information for searching and retrieving the high-level semantic meaningful objects.

References

[1]
Japan Electronics and Information Technology Industries Association. Exchangeable image file format for digital still cameras: Exif version 2.2 (JEITA cp-3451). April 2002.
[2]
Z.-F. Goh and L.-T. Chia. Mpeg7 annotation tool for images & graphics files - II. In Technical report of final year project, Nanyang Technological University, 2004.
[3]
L. Itti and C. Koch. A comparison of feature combination strategies for saliency-based visual attention systems. In Proc. SPIE Human Vision and Electronic Imaging IV (HVEI'99), San Jose, CA, volume 3644, pages 473--482, 1999.
[4]
L. Itti, C. Koch, and E. Niebur. A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11):1254--1259, November 1998.
[5]
Y. Ma and H. Zhang. Contrast-based image attention analysis by using fuzzy growing. In Proc. ACM Multimedia, Berkeley, CA USA, Novemember 2003.
[6]
U. Rutishauser, D. Walther, C. Koch, and P. Perona. Is bottom-up attention useful for object recognition? In Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 37--44. IEEE, July 2004.

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  • (2017) In situ measurements of animal morphological features: A non‐invasive method Methods in Ecology and Evolution10.1111/2041-210X.128989:3(613-623)Online publication date: 17-Oct-2017

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    cover image ACM Conferences
    MULTIMEDIA '05: Proceedings of the 13th annual ACM international conference on Multimedia
    November 2005
    1110 pages
    ISBN:1595930442
    DOI:10.1145/1101149
    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: 06 November 2005

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    MULTIMEDIA '05 Paper Acceptance Rate 49 of 312 submissions, 16%;
    Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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    • (2017) In situ measurements of animal morphological features: A non‐invasive method Methods in Ecology and Evolution10.1111/2041-210X.128989:3(613-623)Online publication date: 17-Oct-2017

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