Hierarchical Saliency Detection

Qiong Yan, Li Xu, Jianping Shi, Jiaya Jia; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 1155-1162

Abstract


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.

Related Material


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[bibtex]
@InProceedings{Yan_2013_CVPR,
author = {Yan, Qiong and Xu, Li and Shi, Jianping and Jia, Jiaya},
title = {Hierarchical Saliency Detection},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2013}
}