graviti logo产品公开数据集关于我们
登录
438
0
16
MSRA10K
创建来自Hello Dataset / Robert
概要
代码
活动

Overview

The MSRA Salient Object Database, which originally provides salient object annotation in terms of bounding boxes provided by 3-9 users, is widely used in salient object detection and segmentation community.

Data Collection

The MSRA10K benchmark dataset (a.k.a. THUS10000) comprises of per-pixel ground truth annotation for 10, 000 MSRA images (181 MB), each of which has an unambiguous salient object and the object region is accurately annotated with pixel wise ground-truth labeling (13.1M). We provide saliency maps (5.3 GB containing 170, 000 image) for our methods as well as other 15 state of the art methods, including FT [1], AIM [2], MSS [3], SEG [4], SeR [5], SUN [6], SWD [7], IM [8], IT [9], GB [10], SR [11], CA [12], LC [13], AC [14], and CB [15]. Saliency segmentation (71.3MB) results for FT[1], SEG[4], and CB[10] are also available.

Citation

@article{ChengPAMI,
  author = {Ming-Ming Cheng and Niloy J. Mitra and Xiaolei Huang and Philip H. S. Torr and
Shi-Min Hu},
  title = {Global Contrast based Salient Region Detection},
  year  = {2015},
  journal= {IEEE TPAMI},
  volume={37},
  number={3},
  pages={569--582},
  doi = {10.1109/TPAMI.2014.2345401},
}


@conference{13iccv/Cheng_Saliency,
  title={Efficient Salient Region Detection with Soft Image Abstraction},
  author={Ming-Ming Cheng and Jonathan Warrell and Wen-Yan Lin and Shuai Zheng
and Vibhav Vineet and Nigel Crook},
  booktitle={IEEE ICCV},
  pages={1529--1536},
  year={2013},
}


@article{SalObjSurvey,
  author = {Ali Borji and Ming-Ming Cheng and Huaizu Jiang and Jia Li},
  title = {Salient Object Detection: A Survey},
  journal = {ArXiv e-prints},
  archivePrefix = {arXiv},
  eprint = {arXiv:1411.5878},
  year = {2014},
}


@article{SalObjBenchmark,
  author = {Ali Borji and Ming-Ming Cheng and Huaizu Jiang and Jia Li},
  title = {Salient Object Detection: A Benchmark},
  journal = {IEEE TIP},
  year={2015},
  volume={24},
  number={12},
  pages={5706-5722},
  doi={10.1109/TIP.2015.2487833},
}
数据集信息
应用场景Common
标注类型Polygon2D
LicenseUnknown
更新时间2021-03-24 22:50:22
数据概要
数据格式Image
数据数量0
已标注数量0
文件大小196KB
版权归属方
College of Computer Science, Nankai University
标注方
未知
了解更多和支持
相关数据集
COCO
创建来自Robert
CSSD
创建来自Robert
Open Images
创建来自Robert
DOTA
创建来自Robert
立即开始构建AI
免费开始联系我们