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

Collections

Department of Computer Engineering, Conference / Seminar