Aiming at the problem of saliency detection, this paper proposes a commute-time distance based sali-ent object detection model to extract important objects of images. First, initial background prior map is generated based on background seeds at image boundary detected by clustering method. Next, salient points are utilized to get a coarse convex hull of salient region, the improved convex hull prior map is obtained based on the saliency seeds extracted from the convex hull. Finally, integrate the two prior maps to get the final saliency map. A novel distance named commute-time distance is employed to measure the feature difference between regions. Experi-mental results show the superiority of commute-time distance over traditional Euclidean distance or geodesic dis-tance. Furthermore, effectiveness of the proposed model over many state-of-the-art methods is illustrated.%针对图像显著性检测问题，提出一种基于通勤距离度量区域显著性，并提取图像中重要目标的方法。首先用聚类算法检测图像边界的背景种子点，构建初始背景先验图；其次利用显著点构建包围显著目标的凸包，提取凸包内前景种子点诱导其他区域的显著性值，从而得到改进的凸包先验图；最后将2个先验图融合得到最终的显著图。该算法中涉及的区域间的特征对比均应用了新颖而鲁棒的通勤距离。实验结果表明，通勤距离能够更准确有效地度量区域间的相似性，比传统的测地距离和欧氏距离更加优越，并优于现有的大多数算法。
展开▼
