Size and Location Matter: A New Baseline for Salient Object Detection

Recent years have seen many complex models proposed for salient object detection and progressing results. However, less has been done to justify the need for such complex models as there lacks sufficient comparison to simple baselines on more challenging datasets. In this work, we propose a new baseline method for saliency detection. It simply considers a large region close to the image center as salient, and defines the saliency of a region as the product of its size and centerness. As accurate image segmentation problem is difficult by itself, we propose novel techniques that can estimate these attributes using superpixels in a soft manner, without the need to perform hard image segmentation. Our approach is based on very simple concepts and implementation, but already achieves very competitive results, especially on challenging datasets. It is further shown highly complementary with the state-of-the-art. Therefore we believe our method serves as a strong baseline and would enhance the problem understanding for future work.