@article{oai:kyutech.repo.nii.ac.jp:00004880,
 author = {Boudissa, Ahmed and Tan, Joo kooi and タン, ジュークイ and Kim, Hyungseop and 金, 亨燮 and Shinomiya, Takashi and Ishikawa, Seiji and 石川, 聖二},
 issue = {1},
 journal = {Biomedical Soft Computing and Human Science},
 month = {Apr},
 note = {In this paper, we present a novel approach to saliency detection. We define a visually salient region in an image with following two properties; global spatial redundancy, i.e., mutual-information, and local saliency, i.e., self-information or simply the region complexity. The former is its probability of occurrence within the image, whereas the latter defines how much information is contained within a region, and it is quantified by the entropy. By combining the global spatial redundancy measure and local entropy, we can achieve a simple, yet robust saliency detector. We evaluate it quantitatively and qualitatively. The comparison to Itti et al. [6], the spectral residual approach by Hou and Zhang [5], Achanta et al. [13] as well as to Zhai and Shah [14], on publicly available data shows a significant improvement.},
 pages = {69--73},
 title = {A Saliency Detection Technique Considering Self- and Mutual-Information},
 volume = {19},
 year = {2014},
 yomi = {イシカワ, セイジ}
}