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论文类型：期刊论文

发表时间：2019-03-14

发表刊物：NEUROCOMPUTING

收录刊物：SCIE、Scopus

卷号：333

页面范围：211-220

ISSN号：0925-2312

关键字：Saliency detection; Multi-scale Pyramid Pooling Network (MPPNet); Convolutional neural networks (CNNs)

摘要：In recent years, visual saliency has witnessed tremendous progress through using deep convolutional neural networks (CNNs). For effective salient object detection, contextual information has been widely employed since the global context can tell different objects apart while the local context can distinguish salient ones from the background. Inspired by this, in this paper we propose a novel Multi-scale Pyramid Pooling Network (MPPNet) by exploiting global and local context in a unified way. This is achieved by incorporating hierarchical local information and global pyramid pooling representation. Particularly, the integration of multi-scale pyramid pooling proves its capacity to produce high-quality prediction map through the use of multiple pooling variables. Quantitative and qualitative experiments demonstrate the effectiveness of the proposed framework. Our method can significantly improve the performance based on four popular benchmark datasets. (C) 2018 Elsevier B.V. All rights reserved.