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Improving Spatial Saliency Using Affinity Model and Temporal Motion

机译:使用亲和力模型和时间运动改善空间显着性

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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 estimatedfrom 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 Thai'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:显着图已应用于各种领域,例如图像分割,对象检测,图像缩放等。在过去的几十年中,已经引入了多种用于静止图像的空间显着性生成方法。近来,运动显着性在利用从图像序列估计的运动数据中引起了极大的兴趣。在本文中,我们提出了一种增强空间显着性的显着性生成方法。基于空间和运动显着性的组合,而不考虑运动分类。此外,为了连接具有不同颜色的邻近像素并获得相似的显着性,集成了亲和力模型。在实验中,我们对七个图像序列集执行了所提出的方法。我们的显着性图与Thai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,我们验证了显着性值比平均空间显着性每像素增加+41。

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