Supervised Deep Learning for Content-Aware Image Retargeting with
  Fourier Convolutions
MohammadHossein Givkashi MohammadReza Naderi Nader Karimi Shahram Shirani Shadrokh Samavi
Image retargeting aims to alter the size of the image with attention to the
contents. One of the main obstacles to training deep learning models for image
retargeting is the need for a vast labeled dataset. Labeled datasets are
unavailable for training deep learning models in the image retargeting tasks.
As a result, we present a new supervised approach for training deep learning
models. We use the original images as ground truth and create inputs for the
model by resizing and cropping the original images. A second challenge is
generating different image sizes in inference time. However, regular
convolutional neural networks cannot generate images of different sizes than
the input image. To address this issue, we introduced a new method for
supervised learning. In our approach, a mask is generated to show the desired
size and location of the object. Then the mask and the input image are fed to
the network. Comparing image retargeting methods and our proposed method
demonstrates the model's ability to produce high-quality retargeted images.
Afterward, we compute the image quality assessment score for each output image
based on different techniques and illustrate the effectiveness of our approach.