Fabric Image Layering Based on Kmeans-AP

Abstract

As a common image stratification algorithm, Kmeans clustering effect is affected by the initial random clustering center. The same parameter is used for different images, and the clustering effect is not the same. It is difficult to meet the standards of industrial production. Therefore, it is very important to improve the Kmeans algorithm to improve the clustering effect. This paper proposes an improved Kmeans algorithm, which is a combination of Kmeans algorithm and AP(Affinity Propagation) algorithm. This algorithm not only avoids the need of Kmeans to select the appropriate K value in advance, but also improves the overall clustering effect. The experimental results show that the clustering effect of Kmeans-AP algorithm proposed in this paper is better than the average effect of Kmeans in 83% of the whole data set.