Abstract
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.
Original language | English |
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Title of host publication | 2017 IEEE International Conference on Multimedia and Expo, ICME 2017 |
Publisher | IEEE Computer Society |
Pages | 1297-1302 |
Number of pages | 6 |
ISBN (Electronic) | 9781509060672 |
DOIs | |
Publication status | Published - 28 Aug 2017 |
Externally published | Yes |
Event | 2017 IEEE International Conference on Multimedia and Expo, ICME 2017 - Hong Kong, Hong Kong Duration: 10 Jul 2017 → 14 Jul 2017 |
Conference
Conference | 2017 IEEE International Conference on Multimedia and Expo, ICME 2017 |
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Country/Territory | Hong Kong |
City | Hong Kong |
Period | 10/07/17 → 14/07/17 |
Keywords
- Saliency detection
- Turbidity
- Underwater database
- Underwater object
ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Science Applications