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Visual Saliency Estimation via Attribute Based Classifiers and Conditional Random Field
Date
2016-05-19
Author
Demirel, Berkan
Cinbiş, Ramazan Gökberk
İKİZLER CİNBİŞ, NAZLI
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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.
Subject Keywords
Top-down saliency estimation
,
Discriminative dictionary
,
Conditional random field
,
Attribute
URI
https://hdl.handle.net/11511/37474
DOI
https://doi.org/10.1109/siu.2016.7495876
Conference Name
24th Signal Processing and Communication Application Conference (SIU)
Collections
Department of Computer Engineering, Conference / Seminar
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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: https://hdl.handle.net/11511/37474.