CAJ | 학술논문

该文针对现有的基于图的流形排序的显著性目标检测方法中仅使用 k-正则图刻画各个节点的空间连接性的不足以及先验背景假设过于理想化的缺陷，提出一种改进的方法，旨在保持高查全率的同时，提高准确率。在构造图模型时，先采用仿射传播聚类将各超像素(节点)自适应地划分为不同的颜色类，在传统的k-正则图的基础上，将属于同一颜色类且空间上位于同一连通区域的各个节点也连接在一起；而在选取背景种子点时，根据边界连接性赋予位于图像边界的超像素不同的背景权重，采用图割方法筛选出真正的背景种子点；最后，采用经典的流形排序算法计算显著性。在常用的MSRA-1000和复杂的SOD数据库上同7种流行算法的4种量化评价指标的实验对比证明了所提改进算法的有效性和优越性。
해문침대현유적기우도적류형배서적현저성목표검측방법중부사용 k-정칙도각화각개절점적공간련접성적불족이급선험배경가설과우이상화적결함，제출일충개진적방법，지재보지고사전솔적동시，제고준학솔。재구조도모형시，선채용방사전파취류장각초상소(절점)자괄응지화분위불동적안색류，재전통적k-정칙도적기출상，장속우동일안색류차공간상위우동일련통구역적각개절점야련접재일기；이재선취배경충자점시，근거변계련접성부여위우도상변계적초상소불동적배경권중，채용도할방법사선출진정적배경충자점；최후，채용경전적류형배서산법계산현저성。재상용적MSRA-1000화복잡적SOD수거고상동7충류행산법적4충양화평개지표적실험대비증명료소제개진산법적유효성화우월성。
To overcome the shortage that the spatial connectivity of every node is modeled only via thek-regular graph and the idealistic prior background assumption is used in existing salient object detection method based on graph-based manifold ranking, an improved method is proposed to increase the precision while preserving the high recall. When constructing the graph model, the affinity propagation clustering is utilized to aggregate the superpixels (nodes) to different color clusters adaptively. Then, based on the traditionalk-regular graph, the nodes belonging to the same cluster and located in the same spatial connected region are connected with edges. According to the boundary connectivity, the superpixels along the image boundaries are assigned with different background weights. Then, the real background seeds are selected by graph cuts method. Finally, the classical manifold ranking method is employed to compute saliency. The experimental comparison results of 4 quantitative evaluation indicators between the proposed and 7 state-of-the-art methods on MSRA-1000 and complex SOD datasets demonstrate the effectiveness and superiority of the proposed improved method.