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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Fast and Efficient Saliency Detection Using Sparse Sampling and Kernel Density Estimation
verfasst von : Hamed Rezazadegan Tavakoli, Esa Rahtu, Janne Heikkilä
Erschienen in: Image Analysis
Verlag: Springer Berlin Heidelberg
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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.