DISC: Deep Image Saliency Computing via Progressive Representation Learning
文献类型：期刊论文
| 作者 | Chen, Tianshui1; Lin, Liang1; Liu, Lingbo1; Luo, Xiaonan1; Li, Xuelong2
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| 刊名 | ieee transactions on neural networks and learning systems
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| 出版日期 | 2016-06-01 |
| 卷号 | 27期号:6页码:1135-1149 |
| 关键词 | Convolutional neural network (CNN) image labeling representation learning saliency detection |
| ISSN号 | 2162-237x |
| 产权排序 | 2 |
| 英文摘要 | salient object detection increasingly receives attention as an important component or step in several pattern recognition and image processing tasks. although a variety of powerful saliency models have been intensively proposed, they usually involve heavy feature (or model) engineering based on priors (or assumptions) about the properties of objects and backgrounds. inspired by the effectiveness of recently developed feature learning, we provide a novel deep image saliency computing (disc) framework for fine-grained image saliency computing. in particular, we model the image saliency from both the coarse- and fine-level observations, and utilize the deep convolutional neural network (cnn) to learn the saliency representation in a progressive manner. in particular, our saliency model is built upon two stacked cnns. the first cnn generates a coarse-level saliency map by taking the overall image as the input, roughly identifying saliency regions in the global context. furthermore, we integrate superpixel-based local context information in the first cnn to refine the coarse-level saliency map. guided by the coarse saliency map, the second cnn focuses on the local context to produce fine-grained and accurate saliency map while preserving object details. for a testing image, the two cnns collaboratively conduct the saliency computing in one shot. our disc framework is capable of uniformly highlighting the objects of interest from complex background while preserving well object details. extensive experiments on several standard benchmarks suggest that disc outperforms other state-of-the-art methods and it also generalizes well across data sets without additional training. the executable version of disc is available online: http://vision.sysu.edu.cn/projects/disc. |
| WOS标题词 | science & technology ; technology |
| 类目[WOS] | computer science, artificial intelligence ; computer science, hardware & architecture ; computer science, theory & methods ; engineering, electrical & electronic |
| 研究领域[WOS] | computer science ; engineering |
| 关键词[WOS] | visual-attention ; object detection ; person reidentification ; region detection ; network |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000377113300003 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/28146]  |
| 专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
| 作者单位 | 1.Sun Yat Sen Univ, Sch Data & Comp Sci, Guangzhou 510006, Guangdong, Peoples R China 2.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr Opt IMagery Anal & Learning OPTIMAL, Xian 710119, Shaanxi, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Tianshui,Lin, Liang,Liu, Lingbo,et al. DISC: Deep Image Saliency Computing via Progressive Representation Learning[J]. ieee transactions on neural networks and learning systems,2016,27(6):1135-1149. |
| APA | Chen, Tianshui,Lin, Liang,Liu, Lingbo,Luo, Xiaonan,&Li, Xuelong.(2016).DISC: Deep Image Saliency Computing via Progressive Representation Learning.ieee transactions on neural networks and learning systems,27(6),1135-1149. |
| MLA | Chen, Tianshui,et al."DISC: Deep Image Saliency Computing via Progressive Representation Learning".ieee transactions on neural networks and learning systems 27.6(2016):1135-1149. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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