Guest Editorial Special Section on Visual Saliency Computing and Learning

Junwei Han, Ling Shao, Nuno Vasconcelos, Jungong Han, Dong Xu

科研成果: 期刊稿件社论

2 引用 (Scopus)

摘要

Vision and multimedia communities have long attempted to enable computers to understand image or video content in a manner analogous to humans. Humans' comprehension to an image or a video clip often depends on the objects that draw their attention. As a result, one fundamental and open problem is to automatically infer the attention attracting or interesting areas in an image or a video sequence. Recently, a large number of researchers explore visual saliency models to address this problem. The study on visual saliency models is originally motivated by simulating humans' bottom-up visual attention and it is mainly based on the biological evidence that humans' visual attention is automatically attracted by highly salient features in the visual scene, which are discriminative with respect to the surrounding environment.

源语言英语
文章编号7470328
页(从-至)1118-1121
页数4
期刊IEEE Transactions on Neural Networks and Learning Systems
27
6
DOI
出版状态已出版 - 6月 2016

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