RGB颜色模型
水准点(测量)
人工智能
计算机科学
Boosting(机器学习)
基本事实
基线(sea)
图形
模式识别(心理学)
任务(项目管理)
图像(数学)
计算机视觉
地图学
地理
地质学
海洋学
经济
理论计算机科学
管理
作者
Guizhao Wang,Chenglong Li,Yunpeng Ma,Aihua Zheng,Jin Tang,Bin Luo
出处
期刊:Communications in computer and information science
日期:2018-01-01
卷期号:: 359-369
被引量:73
标识
DOI:10.1007/978-981-13-1702-6_36
摘要
Despite significant progress, image saliency detection still remains a challenging task in complex scenes and environments. Integrating multiple different but complementary cues, like RGB and Thermal (RGB-T), may be an effective way for boosting saliency detection performance. The current research in this direction, however, is limited by the lack of a comprehensive benchmark. This work contributes such a RGB-T image dataset, which includes 821 spatially aligned RGB-T image pairs and their ground truth annotations for saliency detection purpose. With this benchmark, we propose a novel approach, graph-based multi-task manifold ranking algorithm, for RGB-T saliency detection. Extensive experiments against the baseline methods on the benchmark dataset demonstrate the effectiveness of the proposed approach.
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