Hierarchical Saliency Detection
Refereed conference paper presented and published in conference proceedings


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摘要When dealing with objects with complex structures, saliency detection confronts a critical problem - namely that detection accuracy could be adversely affected if salient foreground or background in an image contains small-scale high-contrast patterns. This issue is common in natural images and forms a fundamental challenge for prior methods. We tackle it from a scale point of view and propose a multi-layer approach to analyze saliency cues. The final saliency map is produced in a hierarchical model. Different from varying patch sizes or downsizing images, our scale-based region handling is by finding saliency values optimally in a tree model. Our approach improves saliency detection on many images that cannot be handled well traditionally. A new dataset is also constructed.
著者Yan Q, Xu L, Shi JP, Jia JY
會議名稱26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
會議開始日23.06.2013
會議完結日28.06.2013
會議地點Portland
會議國家/地區美國
詳細描述organized by IEEE,
出版年份2013
月份1
日期1
出版社IEEE
頁次1155 - 1162
電子國際標準書號978-0-7695-4989-7
國際標準期刊號1063-6919
語言英式英語
Web of Science 學科類別Computer Science; Computer Science, Artificial Intelligence

上次更新時間 2020-16-10 於 23:54