Improved SLIC imagine segmentation algorithm based on K-means

Dividing the image into superpixels contributes to further processing of the image. Simple linear iterative clustering (SLIC) algorithm achieves good segmentation result by clustering color and distance characteristics of pixels. However, finite superpixels easily cause under-segmentation. Therefore, the work corrects segmentation result of SLIC by k-means clustering method calculating similarity based on weighted Euclidean distance. After that, the under-segmentation superpixel blocks are conducted with k-means clustering based on binary classification. Result shows that the corrected SLIC segmentation has better visual effect and index.