针对传统视觉显著性检测算法单纯使用当前观测图像的信息或是先验知识的不足，该文引入了长期特征和短期特征的概念，分别代表先验知识和当前观测图像的信息，并提出了一种基于信息论的算法将它们融合。首先，分别根据人眼跟踪数据和当前观测图像的内容来训练长期和短期稀疏词典并对图像进行稀疏编码，将得到的稀疏编码作为长期和短期特征。其次，针对现有算法只能在整幅图像上或是在一个固定大小的局部邻域内进行统计的缺陷，该文提出一种基于信息熵的特征概率分布估计方法，该方法可以根据当前观测图像的具体情况自适应地选择一个最佳的区域大小来计算长期和短期特征出现的概率。最后，利用香农自信息来输出图像的显著性检测结果。同8种流行算法在公开的人眼跟踪测试库上进行的主观和定量的实验对比证明了该文算法的有效性。%In order for removing the drawback of the traditional visual saliency detection methods which solely used the information of current viewing image or prior knowledge, this paper proposes an information theoretic algorithm to combine the long-term features which imply the prior knowledge with short-term features which imply the information of current viewing image. Firstly, a long-term sparse dictionary and short-term sparse dictionary are trained using the eye-tracking data and current viewing image, respectively. Their corresponding sparse codes are regarded as the long-term and short-term features, respectively. Secondly, to reduce the problem of existing methods which derivated features on the entire image or a local neighborhood with the fixed size, an information entropy based the estimation method of probability distribution of features is proposed. This method can infer an optimal size of region adaptively according to the characteristics of the current viewing image for the calculation of probability of the appearance of long-tern and short-term features. Finally, the saliency map is formulated by Shannon self-information. The subjective and quantitative comparisons with 8 state-of-the-art methods on publicly available eye-tracking databases demonstrate the effectiveness of the proposed method.
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