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.
To identify small high contrast regions, REF propose a multi-layer approach to analyse the saliency cues.
Hierarchical Saliency Detection
2013 IEEE Conference on Computer Vision and Pattern Recognition
2013 IEEE Conference on Computer Vision and Pattern Recognition
Computer Science