Development of a global batch clustering with gradient descent and initial parameters in colour image classification

Abstract

This study addresses two issues from batch clustering using K-means algorithm in colour image classification application. One of the major issues is the drifting phenomenon in the batch clustering due to the stochastic nature of the clustering procedure. Also in literature, the initial parameter is important to direct the clustering algorithm converge to the proper local solution. In this study, a new algorithm is proposed to address these two issues in application. Recently, a research found that the principal component analysis (PCA) result directly indicates the membership of the clusters in K-means algorithm. Hence using this, the first part of the proposed algorithm shows the possibility to estimate the initial parameters accurately for Kmeans with a hierarchical manner of PCA solution. In addition, a gradient descent approach is used for the global batch clustering to reduce the drifting and hence speed up convergence in the refining stage. All necessary proofs and justifications are also provided. The evaluation study has shown that the proposed algorithm performs better than the original K-means clustering algorithms with various initial parameter estimation processes.