提出了一种基于超像素分割的结果进行聚类来检测复制粘贴篡改区域的方法.常规K-means等点聚类方法是直接对点进行聚类分析，而该方法则是将若干随机种子置于图像中，借助于超像素分割方法进行区域分割，随后获得包含预先匹配特征点的区域.所提算法以此区域内匹配特征点的数目作为衡量标准，判定区域内的匹配特征点是否为有效特征点.当匹配点的数目到达某个阈值时则将子区域内的点聚为一类，这样聚类的结果更加贴近图像内容，符合实际情况.实验结果表明，所提方法比传统的SIFT等方法更加有效.
A clustering method to detect the copy-move area based on the results of superpixel segmentation is proposed. Different from the traditional K-means clustering method, the proposed clustering method is to place random seeds in the image and segment the region by using super-pixel segmentation method, and then obtain the regions containing pre-matched feature points. In this algorithm, the number of matched feature points in each region is used as a criterion to determine whether the matched feature points in the region are effective feature points. When the number of matching points reaches a certain threshold, the points in the sub-regions are clustered into one group, so that the clustering results are closer to the image content and in accordance with the actual situation. Experiments show that the proposed method based on super-pixel segmentation is more effective than the traditional SIFT method.
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