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.
REF proposed a multi-layer approach and hierarchical model to detect saliency.
Hierarchical Saliency Detection
2013 IEEE Conference on Computer Vision and Pattern Recognition
2013 IEEE Conference on Computer Vision and Pattern Recognition
Computer Science