Visual Saliency Estimation via Attribute Based Classifiers and Conditional Random Field

Demirel B., CİNBİŞ R. G., İKİZLER CİNBİŞ N.

24th Signal Processing and Communication Application Conference (SIU), Zonguldak, Turkey, 16 - 19 May 2016, pp.861-864, (Full Text) identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/siu.2016.7495876
  • City: Zonguldak
  • Country: Turkey
  • Page Numbers: pp.861-864
  • Hacettepe University Affiliated: Yes

Abstract

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.