  Image retargeting is the task of making images capable of being displayed on
screens with different sizes. This work should be done so that high-level
visual information and low-level features such as texture remain as intact as
possible to the human visual system, while the output image may have different
dimensions. Thus, simple methods such as scaling and cropping are not adequate
for this purpose. In recent years, researchers have tried to improve the
existing retargeting methods and introduce new ones. However, a specific method
cannot be utilized to retarget all types of images. In other words, different
images require different retargeting methods. Image retargeting has a close
relationship to image saliency detection, which is relatively a new image
processing task. Earlier saliency detection methods were based on local and
global but low-level image information. These methods are called bottom-up
methods. On the other hand, newer approaches are top-down and mixed methods
that consider the high level and semantic information of the image too. In this
paper, we introduce the proposed methods in both saliency detection and
retargeting. For the saliency detection, the use of image context and semantic
segmentation are examined, and a novel mixed bottom-up, and top-down saliency
detection method is introduced. After saliency detection, a modified version of
an existing retargeting method is utilized for retargeting the images. The
results suggest that the proposed image retargeting pipeline has excellent
performance compared to other tested methods. Also, the subjective evaluations
on the Pascal dataset can be used as a retargeting quality assessment dataset
for further research.
