中文摘要：

为了提高显著性检测的鲁棒性,增加全局信息和局部信息的联系,提出一种基于稀疏表示和标签传播的显著性检测算法.首先将复杂数据集进行简洁的表达,获得数据间更深层次的全局联系,并利用稀疏表示理论定义邻接矩阵,突破以往具有共同边界的限制,将处于同一子空间的数据点定义为邻居;其次利用图像中每个区域间的相似度计算权值矩阵并构建图模型,然后经过有效筛选部分边界区域获得背景标签;最后基于上述算法获得的图模型和背景标签,应用标签传播算法预测未标记区域的标签信息,获得最终的显著性图.在多个公开的显著性数据库上进行实验,验证了文中算法的有效性.

英文摘要：

To improve the robustness of salient detection and increase the connection between the global information and local information,this paper proposes a salient detection method based on sparse representation and label propagation.First of all,to represent a data set succinctly and obtain a further relationship between the data,we define a new adjacency matrix that considers the regions located in the same subspace of data sets instead of the traditional definition of neighbor which share common boundary by using the sparse theory.Next,the weight matrix is computed by the similarity of the regions in the picture.And then,we select a part of boundary areas as background label.Finally,through weight matrix and background label,we adopt the label propagation to predict the label information of unlabeled region.We get the saliency map at last.Results on five benchmark data sets show that the proposed method achieves superior performance.