THE OUC-VISION LARGE-SCALE UNDERWATER IMAGE DATABASE

被引:0
|
作者
Jian, Muwei [1 ,2 ]
Qi, Qiang [1 ]
Dong, Junyu [1 ]
Yin, Yinlong [2 ]
Zhang, Wenyin [3 ]
Lam, Kin-Man [1 ,4 ]
机构
[1] Ocean Univ China, Dept Comp Sci & Technol, Qingdao, Peoples R China
[2] Shandong Univ Finance & Econ, Sch Comp Sci & Technol, Jinan, Shandong, Peoples R China
[3] Linyi Univ, Sch Informat, Linyi, Peoples R China
[4] Hong Kong Polytech Univ, Ctr Signal Proc, Dept Elect & Informat Engn, Kowloon, Hong Kong, Peoples R China
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
underwater database; underwater object; turbidity; saliency detection; OBJECT DETECTION; SALIENCY; MODEL;
D O I
暂无
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
In this paper, a large-scale underwater image database for underwater salient object detection or saliency detection is presented in detail. This database is called the OUC-VISION underwater image database, which contains 4400 underwater images of 220 individual objects. Each object is captured with four pose variations (the frontal-, the opposite-, the left-, and the right-views of each underwater object) and five spatial locations (the underwater object is located at the top-left corner, the top-right corner, the center, the bottom-left corner, and the bottom-right corner) to obtain 20 images. Meanwhile, this publicly available OUC-VISION database also provides relevant industrial fields, and academic researchers with underwater images under different sources of variations, especially pose, spatial location, illumination, turbidity of water, etc. Ground-truth information is also manually labelled for this database. The OUC-VISION database can not only be widely used to assess and evaluate the performance of the state-of-the-art salient-object detection and saliency-detection algorithms for general images, but also will particularly benefit the development of underwater vision technology in the future.
引用
收藏
页码:1297 / 1302
页数:6
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