Convolution Optimized Density Clustering Algorithm for Image Segmentation

First, convolution is introduced into DBSCAN, three-dimensional convolution is used to obtain the core object, avoiding distance calculation and region retrieval, and reducing the time complexity of this step from the second order to a constant level.

Then, the three-dimensional convolution is optimized based on dynamic programming, so that the convolution time is independent of the size of the convolution kernel. Finally, the neighborhood search range of the sample points is simplified according to the ordered core object set. Compared with the existing algorithms, the experimental results on the Berkeley BSD300 show that the algorithm is effective, the CH index and the time efficiency increase by 17% and 49% on average.