Abstract To improve the performance of saliency detection, a bottom-up saliency object detection model is proposed by integrating different features based manifold ranking and co-connectivity. Aiming at the calculation on edge and connection bewteen nodes of the graph in most manifold ranking based models, a manifold ranking based salient map is produced via different features to calculate the weight of edges and modified connection to construct the graph. Simultaneously, the co-connectivity based salient map is obtained by incorporating boundary connectivity and foreground connectivity. The final saliency map is achieved through fusing these two salient results with different scales. Compared with 16 state-of-the-art methods on four public benchmark datasets, the proposed algorithm performs better.
Fund:Supported by National Natural Science Foundation of China(No.61201323), Natural Science Foundation of Shaanxi Province(No.2017JM6026)
Corresponding Authors:
WANG Yanzhao, Ph.D. candidate. His research interests include computer vison and salient object detection.
About author:: PENG Guohua, Ph.D., professor. His research interests include image processing and computer aided geometric design.YAN Weidong, Ph.D., associate professor. His research interests include image registration, classification and change detection of remote sensing image.
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2019, Vol. 32 
