The OUC-vision large-scale underwater image database
Muwei Jian, Qiang Qi, Junyu Dong, Yinlong Yin, Wenyin Zhang, Kin‐Man Lam · 2017
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.