Saliency detection for panoramic landscape images of outdoor scenes |
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| Affiliation: | 1. LIMSI, CNRS, Univ. Paris-Sud, Université Paris-Saclay, France;2. Sorbonne Universités, UPMC Univ. Paris 06, CNRS UMR 7606, LIP6, France;1. National Institute of Telecommunications and ICT, Oran, Algeria;2. PRISME Laboratory, University of Orléans, France;3. XLIM Laboratory, University of Poitiers, France;1. School of Info. and Cont. Eng., China Univ. of Mining and Technology, China;2. School of Info. and Elec. Eng., Jiangsu Vocational College of Business, China;3. The State Info. Center of P.R. China, China;4. BJUT Faculty of Info. Tech., Beijing University of Technology, China;1. Indian Institute of Information Technology Chittoor, Sri City, India;2. Indian Institute of Information Technology Allahabad, India;1. National Institute of Technology, Uttarakhand, India;2. Malaviya National Institute of Technology, Jaipur, India |
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| Abstract: | Saliency detection has been researched for conventional images with standard aspect ratios, however, it is a challenging problem for panoramic images with wide fields of view. In this paper, we propose a saliency detection algorithm for panoramic landscape images of outdoor scenes. We observe that a typical panoramic image includes several homogeneous background regions yielding horizontally elongated distributions, as well as multiple foreground objects with arbitrary locations. We first estimate the background of panoramic images by selecting homogeneous superpixels using geodesic similarity and analyzing their spatial distributions. Then we iteratively refine an initial saliency map derived from background estimation by computing the feature contrast only within local surrounding area whose range and shape are changed adaptively. Experimental results demonstrate that the proposed algorithm detects multiple salient objects faithfully while suppressing the background successfully, and it yields a significantly better performance of panorama saliency detection compared with the recent state-of-the-art techniques. |
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| Keywords: | Saliency detection Panoramic image Wide fields of view Background estimation Saliency refinement |
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