We present \emph{Deep Image Retargeting} (\emph{DeepIR}), a coarse-to-fine framework for content-aware image retargeting. Our framework first constructs the semantic structure of input image with a deep convolutional neural network. Then a uniform re-sampling that suits for semantic structure preserving is devised to resize feature maps to target aspect ratio at each feature layer. The final retargeting result is generated by coarse-to-fine nearest neighbor field search and step-by-step nearest neighbor field fusion. We empirically demonstrate the effectiveness of our model with both qualitative and quantitative results on widely used RetargetMe dataset.
@article{arxiv.1811.07793,
title = {DeepIR: A Deep Semantics Driven Framework for Image Retargeting},
author = {Jianxin Lin and Tiankuang Zhou and Zhibo Chen},
journal= {arXiv preprint arXiv:1811.07793},
year = {2019}
}