Segmentation-based salient object detection

Abstract

Salient object detection is an important task for both the human perception and computer vision applications. Contrary to the popular pixelor superpixel-based salient object detection methods, we employ high quality segmentation to facilitate salient object detection in this paper. After segmenting the input image using a recent method of gPb-owt-ucm, we easily extract the salient objects from candidate segments only with some very simple intrinsic features of the segments. In addition, for more reasonable performance evaluation, we build a perception based dataset, which contains 499 complex natural images and the corresponding hierarchical salient object ground-truth defined with the assistance of eye-tracker recorded fixations. Experiments on the public ASD dataset and our new dataset show that our segmentation-based salient object detection method (SBSO) achieves competitive performance comparing to some state-of-the-art algorithms.