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, Türkiye, 16 - 19 Mayıs 2016, ss.861-864 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası:
  • Doi Numarası: 10.1109/siu.2016.7495876
  • Basıldığı Şehir: Zonguldak
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.861-864
  • Hacettepe Üniversitesi Adresli: Evet

Özet

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.