鲁棒背景模板显著性检测

Abram W. Makram, N. Salem, M. El-Wakad, W. Al-Atabany
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摘要

本文针对优化后的背景模板,提出了一种有效的基于密集稀疏表示的显著性检测方法。首先,将输入图像划分为紧凑和均匀的超像素;然后,通过引入边界电导率测量来生成优化背景模板,以改进图像超像素在优化背景中的密集和稀疏表示,其中重建误差表示显著性度量。在优化模板的基础上,通过密集和稀疏表示生成两个显著性映射。最后,利用贝叶斯框架对两个显著性图进行整合,得到最终的显著性图。实验结果表明,该方法与八种最先进的方法相比具有良好的性能。此外,该方法在突出图像边界上具有挑战性的突出物体方面更为有效。
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Robust Background Template for Saliency Detection
In this paper, we propose an effective saliency detection method based on dense and sparse representation in-terms of an optimized background template. Firstly, the input image is divided into compact and uniform super-pixels. Then, the optimized background template is produced by introducing boundary conductivity measurement to improve the dense and sparse representation of the image's super-pixels in terms of the optimized background, where the reconstruction error represents a saliency measure. Based on the optimized template, two saliency maps are generated by dense and sparse representation. Finally, the Bayesian framework used to integrate the two saliency maps to obtain the final one. Experimental results show that the proposed method performs favorably against eight state-of-the-art methods. In addition, the proposed method is shown to be more effective in highlighting the challenging salient objects that touch the image boundary.
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