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	Deep Specialized Network for Illuminant Estimation
	Wu Shi; Chen Change Loy; Xiaoou Tang
	2016
会议名称	ECCV2016
会议地点	荷兰阿姆斯特丹
英文摘要	Illuminant estimation to achieve color constancy is an ill- posed problem. Searching the large hypothesis space for an accurate illu- minant estimation is hard due to the ambiguities of unknown re ections and local patch appearances. In this work, we propose a novel Deep Specialized Network (DS-Net) that is adaptive to diverse local regions for estimating robust local illuminants. This is achieved through a new convolutional network architecture with two interacting sub-networks, i.e. an hypotheses network (HypNet) and a selection network (SelNet). In particular, HypNet generates multiple illuminant hypotheses that inher- ently capture di erent modes of illuminants with its unique two-branch structure. SelNet then adaptively picks for con dent estimations from these plausible hypotheses. Extensive experiments on the two largest color constancy benchmark datasets show that the proposed `hypoth- esis selection' approach is e ective to overcome erroneous estimation. Through the synergy of HypNet and SelNet, our approach outperforms state-of-the-art methods such as [1{3].
收录类别	EI
语种	英语
内容类型	会议论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/10026]
专题	深圳先进技术研究院_集成所
作者单位	2016
推荐引用方式 GB/T 7714	Wu Shi,Chen Change Loy,Xiaoou Tang. Deep Specialized Network for Illuminant Estimation[C]. 见:ECCV2016. 荷兰阿姆斯特丹.

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