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Improving Spatial Saliency Using Affinity Model and Temporal Motion

机译:使用亲和模型和时间运动提高空间显着性

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Saliency map has been applied in diverse fields such as image segmentation, object detection, image scaling and so forth. Over the past decades, a variety of spatial saliency generation methods for still images have been introduced. Recently, motion saliency has gained much interest where motion data estimated from an image sequence are utilized In this paper, we propose the saliency generation method that enhances the spatial saliency based on the combination of spatial and motion saliencies without the consideration of motion classification. Further, an affinity model is integrated for the purpose of connecting close-by pixels with different colors and obtaining a similar saliency. In experiment, we performed the proposed method on seven image sequence sets. Our saliency map is compared with Zhai's method for evaluating the saliency improvement. Further, from the objective performance evaluation, we validated that the saliency value increases by +41 per a pixel over the spatial saliency on the average.
机译:显着图已在不同的领域应用,例如图像分段,对象检测,图像缩放等。在过去几十年中,已经介绍了静止图像的各种空间显着发电方法。近来,在本文中利用了从图像序列估计的运动数据的运动显着性,我们提出了显着发电方法,其基于空间和运动拆卸的组合而不考虑运动分类来提高空间显着性。此外,为亲和模型集成,以便以不同的颜色连接逐个像素并获得相似的显着性。在实验中,我们在七个图像序列集上进行了所提出的方法。我们的显着性图与Zhai评估显着性改进的方法进行了比较。此外,从客观性能评估中,我们验证了平均空间显着性在每像素上增加+41的显着值。

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