标题：
Saliency detection using adaptive background template
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所属单位：
计算机科学与技术学院/人工智能学院/软件学院
发表刊物：
IET COMPUTER VISION
关键字：
REGION DETECTION OBJECT DETECTION SUPERPIXELS MODEL
摘要：
Since most existing saliency detection models are not suitable for the condition that the salient objects are near at the image border, the authors propose a saliency detection approach based on adaptive background template (SCB) despite of the position of the salient objects. First, a selection strategy is presented to establish the adaptive background template by removing the potential saliency superpixels from the image border regions, and the initial saliency map is obtained. Second, a propagation mechanism based on K-means algorithm is designed for maintaining the neighbourhood coherence of the above saliency map. Finally, a new spatial prior is presented to integrate the saliency detection results by aggregating two complementary measures such as image centre preference and the background template exclusion. Comprehensive evaluations on six benchmark datasets indicate that the authors' method outperforms other state-of-the-art approaches. In addition, a new dataset containing 300 challenging images is constructed for evaluating the performance of various salient object detection methods.
ISSN号：
1751-9632
是否译文：
否
发表时间：
2017-09-01
合写作者：
Lin Huafeng,Zhou Peiyun,Liang Dachuan,Li Dongmin
通讯作者：
李静,李静
发表时间：
2017-09-01
学位：工学博士学位
职称：副教授
所在单位：计算机科学与技术学院/人工智能学院/软件学院