Fast and Efficient Saliency Detection Using Sparse Sampling and Kernel Density Estimation

  • Hamed Rezazadegan Tavakoli
  • Esa Rahtu
  • Janne Heikkilä
Conference paper

DOI: 10.1007/978-3-642-21227-7_62

Volume 6688 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Rezazadegan Tavakoli H., Rahtu E., Heikkilä J. (2011) Fast and Efficient Saliency Detection Using Sparse Sampling and Kernel Density Estimation. In: Heyden A., Kahl F. (eds) Image Analysis. SCIA 2011. Lecture Notes in Computer Science, vol 6688. Springer, Berlin, Heidelberg

Abstract

Salient region detection has gained a great deal of attention in computer vision. It is useful for applications such as adaptive video/image compression, image segmentation, anomaly detection, image retrieval, etc. In this paper, we study saliency detection using a center-surround approach. The proposed method is based on estimating saliency of local feature contrast in a Bayesian framework. The distributions needed are estimated particularly using sparse sampling and kernel density estimation. Furthermore, the nature of method implicitly considers what refereed to as center bias in literature. Proposed method was evaluated on a publicly available data set which contains human eye fixation as ground-truth. The results indicate more than 5% improvement over state-of-the-art methods. Moreover, the method is fast enough to run in real-time.

Keywords

Saliency detection discriminant center-surround eye-fixation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Hamed Rezazadegan Tavakoli
    • 1
  • Esa Rahtu
    • 1
  • Janne Heikkilä
    • 1
  1. 1.Machine Vision Group, Department of Electrical and Information EngineeringUniversity of OuluFinland