poster

Learning to Assess Image Retargeting

Authors:

Bahetiyaer Bare,

Bo YanAuthors Info & Claims

MM '14: Proceedings of the 22nd ACM international conference on Multimedia

Pages 925 - 928

https://doi.org/10.1145/2647868.2654957

Published: 03 November 2014 Publication History

Abstract

Content-aware image retargeting enables images to fit different devices with various aspect ratios while preserving salient contents. Meanwhile, assessing the quality of image retargeting and unifying both subjective and objective evaluation have become a prominent challenge. In this paper, we propose an image quality assessment based on Radial Basis Function (RBF) neural network. We propose a new feature of image retargeting evaluation, which adapts structural similarity (SSIM) and saliency. By also including other existing features, we build a neural network to assess the quality of the retargeted image. The neural network is trained to combine the above-mentioned features. The accuracy of our proposed assessment is verified by simulations and it possesses huge practical significance.

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Cited By

Tang FDong WMeng YMa CWu FLi XLee T(2020)Image RetargetabilityIEEE Transactions on Multimedia10.1109/TMM.2019.293262022:3(641-654)Online publication date: Mar-2020
https://doi.org/10.1109/TMM.2019.2932620
Tan WYan B(2016)A survey on high coherence visual media retargetingFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-016-6084-310:5(778-796)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1007/s11704-016-6084-3

Index Terms

Learning to Assess Image Retargeting
1. Computing methodologies
  1. Computer graphics
    1. Image manipulation
    2. Rendering

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Information & Contributors

Information

Published In

cover image ACM Conferences

MM '14: Proceedings of the 22nd ACM international conference on Multimedia

November 2014

1310 pages

ISBN:9781450330633

DOI:10.1145/2647868

General Chairs:
Kien A. Hua
University of Central Florida, USA
,
Yong Rui
Microsoft Research, China
,
Ralf Steinmetz
Technische Universitt Darmstadt, Germany
,
Program Chairs:
Alan Hanjalic
Delft University of Technology, Netherlands
,
Apostol (Paul) Natsev
Google, USA
,
Wenwu Zhu
Tsinghua University, China

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]

Sponsors

SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 November 2014

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National Natural Science Foundation of China

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MM '14

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SIGMM

MM '14: 2014 ACM Multimedia Conference

November 3 - 7, 2014

Florida, Orlando, USA

Acceptance Rates

MM '14 Paper Acceptance Rate 55 of 286 submissions, 19%;

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

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Citations

Cited By

Tang FDong WMeng YMa CWu FLi XLee T(2020)Image RetargetabilityIEEE Transactions on Multimedia10.1109/TMM.2019.293262022:3(641-654)Online publication date: Mar-2020
https://doi.org/10.1109/TMM.2019.2932620
Tan WYan B(2016)A survey on high coherence visual media retargetingFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-016-6084-310:5(778-796)Online publication date: 1-Oct-2016
https://dl.acm.org/doi/10.1007/s11704-016-6084-3

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