Chapter

Image Analysis

Volume 6688 of the series Lecture Notes in Computer Science pp 666-675

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

  • Hamed Rezazadegan TavakoliAffiliated withMachine Vision Group, Department of Electrical and Information Engineering, University of Oulu
  • , Esa RahtuAffiliated withMachine Vision Group, Department of Electrical and Information Engineering, University of Oulu
  • , Janne HeikkiläAffiliated withMachine Vision Group, Department of Electrical and Information Engineering, University of Oulu

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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