Image Retargetability

Fan Tang, Weiming Dong, Yiping Meng, Chongyang Ma, Fuzhang Wu, Xinrui Li, Tong Yee Lee

Research output: Contribution to journalArticle

Abstract

Real-world applications could benefit from the ability to automatically retarget an image to different aspect ratios and resolutions while preserving its visually and semantically important content. However, not all images can be equally processed. This study introduces the notion of image retargetability to describe how well a particular image can be handled by content-aware image retargeting. We propose to learn a deep convolutional neural network to rank photo retargetability, in which the relative ranking of photo retargetability is directly modeled in the loss function. Our model incorporates the joint learning of meaningful photographic attributes and image content information, which can facilitate the regularization of the complicated retargetability rating problem. To train and analyze this model, we collect a dataset that contains retargetability scores and meaningful image attributes assigned by six expert raters. The experiments demonstrate that our unified model can generate retargetability rankings that are highly consistent with human labels. To further validate our model, we show the applications of image retargetability in retargeting method selection, retargeting method assessment and generating a photo collage.

Original languageEnglish
Article number8784158
Pages (from-to)641-654
Number of pages14
JournalIEEE Transactions on Multimedia
Volume22
Issue number3
DOIs
Publication statusPublished - 2020 Mar

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Media Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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    Tang, F., Dong, W., Meng, Y., Ma, C., Wu, F., Li, X., & Lee, T. Y. (2020). Image Retargetability. IEEE Transactions on Multimedia, 22(3), 641-654. [8784158]. https://doi.org/10.1109/TMM.2019.2932620