A scalable and efficient method for salient region detection using sampled template collation
Andreas Holzbach, Gordon Cheng · 2014
We propose a fast method for salient region detection which aims at providing a computationally efficient method for online image processing. It is scalable and can be adjusted on the run to adapt to different computational requirements, which makes it a perfect candidate for time crucial applications. In our approach, we apply a template sampling over the image and compare these templates with each other by calculating a dissimilarity score. Templates with a low overall response are therefore likely to be part of a salient region in the image. This conceptually easy method is simple to implement and still outperforms state-of-the-art salient region detection systems (Our model's AUC(ROC) Score 0.794-AIM 0.772).