Saliency detection by multi-context deep learning
Refereed conference paper presented and published in conference proceedings

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摘要Low-level saliency cues or priors do not produce good enough saliency detection results especially when the salient object presents in a low-contrast background with confusing visual appearance. This issue raises a serious problem for conventional approaches. In this paper, we tackle this problem by proposing a multi-context deep learning framework for salient object detection. We employ deep Convolutional Neural Networks to model saliency of objects in images. Global context and local context are both taken into account, and are jointly modeled in a unified multi-context deep learning framework. To provide a better initialization for training the deep neural networks, we investigate different pre-training strategies, and a task-specific pre-training scheme is designed to make the multi-context modeling suited for saliency detection. Furthermore, recently proposed contemporary deep models in the ImageNet Image Classification Challenge are tested, and their effectiveness in saliency detection are investigated. Our approach is extensively evaluated on five public datasets, and experimental results show significant and consistent improvements over the state-of-the-art methods.
著者Zhao R., Ouyang W., Li H., Wang X.
會議名稱IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015
會議開始日07.06.2015
會議完結日12.06.2015
會議地點Boston
會議國家/地區美國
詳細描述organized by IEEE Computer Society
出版年份2015
月份10
日期14
卷號07-12-June-2015
頁次1265 - 1274
國際標準書號9781467369640
國際標準期刊號1063-6919
語言英式英語

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