In this thesis, it is found that DBSCAN algorithm is necessary to calculate the similarity information for many data-points in the neighborhood of every data-point when execute the algorithm.
本文通过对密度聚类算法DBSCAN的研究分析,发现该算法在执行的过程中需要为每个数据点计算临域内若干个数据点的相似度信息。
参考来源 - 一种基于网格的密度聚类算法研究及应用·2,447,543篇论文数据，部分数据来源于NoteExpress
最后，融合各个块的相似度信息获得最终的图像质量评价结果。
Finally, the ultimate image quality is calculated by combining structural similarities of all blocks according to their weights.
小范围用户测试结果表明：融入了链接相似度信息之后，提升了搜索结果的用户满意度。
The small-scale test results show that it enhances the customer satisfaction when we use the link similarity.
该算法首先计算人脸图像的相似度信息，并对得到的相似度图像进行二值化，从而标定人脸区域。
First the algorithm calculates the similarity information and gets the binary image to locate the face region.
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