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Saliency detection based on superpixel correlation and cosine window filtering

机译：基于Superpixel相关性和余弦窗滤波的显着性检测

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摘要

With the development of computer vision, image salient region detection plays an important role in the field of image processing. However, existing saliency detection methods can only generate saliency maps without achieving accuracy, thereby ignoring the integrity of the significant target. The present study proposed a saliency detection algorithm based on the correlation of superpixel and cosine window filtering (SPC) algorithm that introduced a variable weight superpixel segmentation method to keep the edge of information and enhance the integration of the salient target. Space distance and color distance are used to judge the correlation between two superpixels in the proposed algorithm, and based on the superpixel correlation, the saliency map is obtained by all superpixels' voting. When the saliency map is corrected by a cosine window, we call this operation a cosine window filtering. Finally, we used the OSTU method for binarization and obtained the target position and size, using connected domain detection. The experiment results showed that the SPC algorithm can describe superpixels' significance accurately and extract salient object. As compared with other algorithms, our algorithm offers higher accurate results, regarding the accurate position and size of salient targets.
机译：随着计算机视觉的发展，图像突出区域检测在图像处理领域起着重要作用。然而，现有的显着性检测方法只能在不实现精度的情况下产生显着图，从而忽略了重要目标的完整性。本研究提出了一种基于超像素和余弦窗口滤波（SPC）算法相关性的显着性检测算法，其引入了可变权重Superpixel分段方法，以保持信息的边缘并增强突出目标的集成。空间距离和颜色距离用于判断所提出的算法中的两个超像素之间的相关性，并且基于Superpixel相关性，所以通过所有超像素的投票获得显着图。当余弦窗口纠正显着图时，我们调用此操作余弦窗口过滤。最后，我们使用了二值化的OSTU方法，并使用连接域检测获得目标位置和大小。实验结果表明，SPC算法可以准确地描述Superpixels的意义并提取突出物体。与其他算法相比，我们的算法提供更高的准确结果，就突出目标的准确位置和大小提供了更高的准确结果。

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