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A Fast and Robust Image Segmentation Method Based on Superpixels

机译:基于超像素的快速鲁棒图像分割方法

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This paper proposes a novel fast and robust image segmentation method based on superpixels (FRISS). In order to make the algorithm adaptive as well as efficient, we first compute superpixels of the image with modified SLIC. Moreover, a modified SimHash is encoded for each superpixels. In addition, similar superpixels are associated together according to the similarity measure gotten from the Hamming distance of SimHash. FRISS can segment image with the given threshold of the similarity, which demonstrates its' adaptation. On the other hand, the similarity is computed by the Hamming distance of SimHash code which is much faster than other similarities. From the experimental results, we can know that FRISS is fast and efficient.
机译:本文提出了一种新颖的基于超像素的快速鲁棒图像分割方法(FRISS)。为了使该算法具有自适应性和高效性,我们首先使用修改后的SLIC计算图像的超像素。此外,针对每个超像素对修改的SimHash进行编码。另外,根据从SimHash的汉明距离获得的相似性度量,将相似的超像素关联在一起。 FRISS可以按照给定的相似度阈值对图像进行分割,从而证明其适应性。另一方面,相似度是通过SimHash代码的汉明距离来计算的,该距离比其他相似度要快得多。从实验结果可以看出,FRISS是快速有效的。

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