详细信息
Salient object detection via color and texture cues ( SCI-EXPANDED收录 EI收录)
文献类型:期刊文献
英文题名:Salient object detection via color and texture cues
作者:Zhang, Qing[1];Lin, Jiajun[2];Tao, Yanyun[3];Li, Wenju[1];Shi, Yanjiao[1]
机构:[1]Shanghai Inst Technol, Sch Comp Sci & Informat Engn, Shanghai 201418, Peoples R China;[2]East China Univ Sci & Technol, Inst Automat, Shanghai 200237, Peoples R China;[3]Soochow Univ, Sch Urban Rail & Transportat, Suzhou 215137, Peoples R China
年份:2017
卷号:243
起止页码:35
外文期刊名:NEUROCOMPUTING
收录:;EI(收录号:20171103431218);WOS:【SCI-EXPANDED(收录号:WOS:000399511900004)】;
基金:This work is supported by the National Natural Science Foundation of China under Grant nos. 61401281 and 61502327, and Science Foundation of Shanghai under Grant no. 14ZR1440700.
语种:英文
外文关键词:Salient object detection; Background prior; Manifold ranking; Color and texture cues; Affinity propagation clustering
摘要:In this paper, we present a new bottom-up salient object detection approach by constructing two graphs using color and texture features within the manifold ranking framework. First, we calculate the saliency of boundary patches and exclude the ones with high saliency which might be a part of saliency object. Second, we adopt a two-stage scheme for salient detection via affinity propagation clustering and graph-based manifold ranking. The background-based saliency detection aims to obtain the salient object regions as much as possible. In the foreground-based saliency detection, a similar computation is processed as that in the former step and yet slightly different. Instead of simultaneously using all the extracted boundary patches or foreground patches as queries, we compute saliency by using the patches in each cluster in turn and integrating them. At last, the final saliency map is generated by linearly combining two saliency maps respectively exploring color and texture cues. Both qualitative and quantitative evaluations on three publicly available datasets demonstrate the robustness and efficiency of our proposed approach against 21 state-of-the-art methods. (c) 2017 Elsevier B.V. All rights reserved.
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