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


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AbstractWhen 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.
All Author(s) ListYan Q, Xu L, Shi JP, Jia JY
Name of Conference26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Start Date of Conference23/06/2013
End Date of Conference28/06/2013
Place of ConferencePortland
Country/Region of ConferenceUnited States of America
Detailed descriptionorganized by IEEE,
Year2013
Month1
Day1
PublisherIEEE
Pages1155 - 1162
eISBN978-0-7695-4989-7
ISSN1063-6919
LanguagesEnglish-United Kingdom
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence

Last updated on 2020-16-10 at 23:54