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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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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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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: https://hdl.handle.net/11511/37474.