Abstract

Many applications are serviced by the Voronoi tessellation required to split image into Voronoi regions. An automatic method to learn and detect salient region for color image with support of the Voronoi diagram is presented. Salient regions are modeled as flexible circumstance corresponding to centers of mass. The centers are predicted by local contrast-based representation with local maxima. Results are demonstrated that are very competitive with other recent saliency map detection schemes and show robustness to capture visual attention objects. Our major contributions are the local maxima based method for allocation of Voronoi centroids and the Gaussian-based filter for estimating attention degrees. To show the effectiveness of the approach, saliency maps are detected for images of MSRA saliency object database by some state-of-the-art methods. The strengths and the weaknesses of the approach are considered, with a special focus on the context based salient regions ? a challenging task which can be found in wide range of applications addressed in computer vision.

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