Abstract
In this paper we propose an automatic salient object extraction method for nature scene. The proposed method first utilizes an algorithm based on visual attention model to obtain a prior knowledge for Graph Cut, and then constructs the weighted graph of Graph Cut based on super-pixels pre-segmented by the improved watershed algorithm in order to accelerate the speed of proposed method. In this framework, Visual saliency map is obtained using chrominance and intensity features in HSV color space, which provides the approximate region that contains salient object to be segmented. Then the salient object region after extension is cropped as input image, and pre-segmented by the improved watershed algorithm into several regions to construct weighted graph. Finally the salient object is obtained by Graph Cut algorithm. Experiment results show that our algorithm can automatically get salient object without human interactions, and speed up the segmentation without decreasing segmentation accuracy.
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Wang, Z., Su, J., Chen, Y., Gong, S. (2012). Automatic Object Extraction in Nature Scene Based on Visual Saliency and Super Pixels. In: Lei, J., Wang, F.L., Deng, H., Miao, D. (eds) Artificial Intelligence and Computational Intelligence. AICI 2012. Lecture Notes in Computer Science(), vol 7530. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33478-8_68
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DOI: https://doi.org/10.1007/978-3-642-33478-8_68
Publisher Name: Springer, Berlin, Heidelberg
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