Salient object detection has been attracting a lot of interest, and recently
various heuristic computational models have been designed. In this paper, we
formulate saliency map computation as a regression problem. Our method, which
is based on multi-level image segmentation, utilizes the supervised learning
approach to map the regional feature vector to a saliency score. Saliency
scores across multiple levels are finally fused to produce the saliency map.
The contributions lie in two-fold. One is that we propose a discriminate
regional feature integration approach for salient object detection. Compared
with existing heuristic models, our proposed method is able to automatically
integrate high-dimensional regional saliency features and choose discriminative
ones. The other is that by investigating standard generic region properties as
well as two widely studied concepts for salient object detection, i.e.,
regional contrast and backgroundness, our approach significantly outperforms
state-of-the-art methods on six benchmark datasets. Meanwhile, we demonstrate
that our method runs as fast as most existing algorithms.

本文提出一种基于多层图像分割的监督学习方法，将区域特征向量映射到显著性得分，最终融合多个级别的得分来产生显著图，同时通过研究区域对比度和背景性质，实现了在六个基准数据集上优于现有方法的显著物体检测算法。