Visual Saliency Estimation via Attribute Based Classifiers and Conditional Random Field

Demirel, Berkan
Cinbiş, Ramazan Gökberk
Visual Saliency Estimation is a computer vision problem that aims to find the regions of interest that are frequently in eye focus in a scene or an image. Since most computer vision problems require discarding irrelevant regions in a scene, visual saliency estimation can be used as a preprocessing step in such problems. In this work, we propose a method to solve top-down saliency estimation problem using Attribute Based Classifiers and Conditional Random Fields (CRF). Experimental results show that attribute-based classifiers encode visual information better than low level features and the presented approach generates promising results compared to state-of-theart approaches on Graz-02 dataset.
24th Signal Processing and Communication Application Conference (SIU)


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Citation Formats
B. Demirel, R. G. Cinbiş, and N. İKİZLER CİNBİŞ, “Visual Saliency Estimation via Attribute Based Classifiers and Conditional Random Field,” presented at the 24th Signal Processing and Communication Application Conference (SIU), Zonguldak, Turkey, 2016, Accessed: 00, 2020. [Online]. Available: