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http://dx.doi.org/10.3837/tiis.2015.03.018

An Improved Saliency Detection for Different Light Conditions  

Ren, Yongfeng (College of Computer and Information, Hohai University)
Zhou, Jingbo (Faculty of Computer Engineering, Huaiyin Institute of Technology)
Wang, Zhijian (College of Computer and Information, Hohai University)
Yan, Yunyang (Faculty of Computer Engineering, Huaiyin Institute of Technology)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.9, no.3, 2015 , pp. 1155-1172 More about this Journal
Abstract
In this paper, we propose a novel saliency detection framework based on illumination invariant features to improve the accuracy of the saliency detection under the different light conditions. The proposed algorithm is divided into three steps. First, we extract the illuminant invariant features to reduce the effect of the illumination based on the local sensitive histograms. Second, a preliminary saliency map is obtained in the CIE Lab color space. Last, we use the region growing method to fuse the illuminant invariant features and the preliminary saliency map into a new framework. In addition, we integrate the information of spatial distinctness since the saliency objects are usually compact. The experiments on the benchmark dataset show that the proposed saliency detection framework outperforms the state-of-the-art algorithms in terms of different illuminants in the images.
Keywords
Saliency detection; Local sensitive histograms; Illumination invariant features; Different light conditions;
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