Supervised Deep Learning for Content-Aware Image Retargeting with Fourier Convolutions

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.Comment: 18 pages, 5 figure

Hybrid Image Retargeting Using Optimized Seam Carving and Scaling

We present a novel hybrid scheme for content-aware image retargeting that allows retargeting images into arbitrary dimensions while preserving visually prominent features and minimizing global information loss. One of the novelties in our scheme is an optimized importance map incorporating the impacts of the gradient map, context-aware saliency map, skin map and Canny edge map. Another novelty is a systematic utilization of both seam carving and scaling for a good balance between information loss and image stretching, where the number of seam operations along each dimension is adaptively determined by a non-linear optimization process. Furthermore, a switching factor is added to the optimization for interactive user control of the switching point between information loss and image stretching. In addition, we propose an optional step to accelerate seam carving by restricting the optimal seam search to a down-sampled thumbnail and the local regions of the input image