caa a22 4500 605447462 CHVBK 20210128100132.0 cr unu---uuuuu 210128e20150601xx s 000 0 eng 10.1007/s11042-014-2043-x doi (NATIONALLICENCE)springer-10.1007/s11042-014-2043-x A saliency detection model using shearlet transform [Elektronische Daten] [Lei Bao, Jianjiang Lu, Yang Li, Yanwei Shi] Visual attention is a mechanism to derive possible locations of objects or regions from natural scenes, and many studies have tried to simulate this mechanism to build saliency detection models, which would accelerate the course of many applications, such as object location, detection and recognition, image segmentation, retrieval and so on. Recently, researchers have tried building the detection models in transform domains. In this paper, a novel saliency detection model using shearlet transform is presented. Firstly, multi-scale feature maps are created. The feature maps built on scaling coefficients are used to generate potential salient regions, which is further used to update the feature maps generated on shearlet coefficients. As these feature maps represent the details of image in multi scale, based on them global and local contrast is calculated to form global and local saliency maps. That is the proposed model obtains the global saliency based on global probability density distribution, and measures the local saliency by calculating the entropy of local areas. By combining the local and global saliency maps, the final saliency maps are obtained. The work of this paper is absolutely a new try to detect saliency regions in shearlet domain, and experimental results demonstrate the saliency detection performance of the novel proposed model. Springer Science+Business Media New York, 2014 Saliency detection nationallicence Shearlet transform nationallicence Feature map nationallicence Entropy nationallicence Bao Lei College of Command Information Systems, PLA University of Science and Technology, 210000, Qingdao, China aut Lu Jianjiang College of Command Information Systems, PLA University of Science and Technology, 210000, Qingdao, China aut Li Yang College of Command Information Systems, PLA University of Science and Technology, 210000, Qingdao, China aut Shi Yanwei Training Command, PLA 91206 Troops, 266000, Qingdao, China aut Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/11(2015-06-01), 4045-4058 1380-7501 74:11<4045 2015 74 11042 https://doi.org/10.1007/s11042-014-2043-x text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11042-014-2043-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Bao Lei College of Command Information Systems, PLA University of Science and Technology, 210000, Qingdao, China aut NATIONALLICENCE 700 1- Lu Jianjiang College of Command Information Systems, PLA University of Science and Technology, 210000, Qingdao, China aut NATIONALLICENCE 700 1- Li Yang College of Command Information Systems, PLA University of Science and Technology, 210000, Qingdao, China aut NATIONALLICENCE 700 1- Shi Yanwei Training Command, PLA 91206 Troops, 266000, Qingdao, China aut NATIONALLICENCE 773 0- Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/11(2015-06-01), 4045-4058 1380-7501 74:11<4045 2015 74 11042