Saliency prediction can be regarded as the human spontaneous activity. The most effective saliency model should highly approximate the response of viewers to the perceived information. In the paper, we exploit the perception response for saliency detection and propose a heuristic framework to predict salient region. First, to find the perceptually meaningful salient regions, an orientation selectivity based local feature and a visual Acuity based global feature are proposed to jointly predict candidate salient regions. Subsequently, to further boost the accuracy of saliency map, we introduce a visual error sensitivity based operator to activate the meaningful salient regions from a local and global perspective. In addition, an adaptive fusion method based on free energy principle is designed to combine the sub-saliency maps from each image channel to obtain the final saliency map. Experimental results on five natural and emotional datasets demonstrate the superiority of the proposed method compared to twelve state-of-the-art algorithms.

显着性预测可以视为人类的自发活动。最有效的显着性模型应高度近似观众对感知信息的反应。在本文中，我们利用感知响应来进行显着性检测，并提出了一种启发式框架来预测显着区域。首先，为了找到在感知上有意义的显着区域，提出了一种基于方向选择性的局部特征和基于视敏度的全局特征来共同预测候选显着区域。随后，为了进一步提高显着性图的准确性，我们引入了基于视觉错误敏感性的算子，以从局部和全局角度激活有意义的显着区域。此外，设计了一种基于自由能原理的自适应融合方法，将每个图像通道的次显着图进行组合，得到最终显着图。与十二种最新算法相比，在五个自然和情感数据集上的实验结果证明了该方法的优越性。