DeepIR: A Deep Semantics Driven Framework for Image Retargeting
This work addresses content-aware image resizing for applications like web design or media adaptation, but it appears incremental as it builds on existing retargeting methods with a new deep learning approach.
The authors tackled image retargeting by proposing DeepIR, a coarse-to-fine framework that uses deep convolutional neural networks to preserve semantic structure, achieving effectiveness demonstrated with qualitative and quantitative results on the RetargetMe dataset.
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.