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Enhanced Saliency Prediction via Orientation Selectivity

机译:通过方向选择性提高显着性预测

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Saliency prediction can be treated as the activity of the human visual system (HVS). The most effective method should highly approximate the response of HVS to the perceived information. Motivated by that orientation selectivity (OS) mechanism occuring in primary visual cortex (PVC) tells us how the HVS extracts visual information for scene understanding, we propose a novel saliency model by combining an orientation selectivity based local feature called "excitement" map and a visual acuity based global feature called "acuity" map. Further, a saliency augmented operator based on visual error sensitivity is designed to enhance the saliency map. Experimental results on three benchmark databases demonstrate the superior performance of the proposed method compared to ten classical/ state-of-the-art algorithms.
机译:显着性预测可以被视为人类视觉系统(HVS)的活性。最有效的方法应该高度近似HVS对感知信息的响应。通过该定向选择性(OS)机制发生在主视觉皮层(PVC)中,告诉我们HVS如何提取场景理解的可视信息,我们通过组合基于定向选择性的本地特征来提出一种新颖的显着模型,称为“兴奋”映射和一个基于视力的基于敏锐的全局特征,称为“敏锐度”映射。此外,基于视觉误差灵敏度的显着增强操作员旨在增强显着图。三个基准数据库上的实验结果表明,与十个经典/最先进的算法相比,所提出的方法的卓越性能。

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