Automated clustering by support vector machines with a local-search strategy and its application to image segmentation

Deciding a rational number of clusters for the problems to be solved and determining the parameters associated with the clustering algorithms are two critical issues in the configuration of data clustering. Usually, a manual trial-and-error manner is used to induce a feasible configuration. This paper presents an automated clustering method, which determines the clustering configuration automatically. The proposed method is based on the techniques of support-vector machines with a local-search strategy. It starts with running one-class support-vector machines (OCSVM) to partition input data into a random number of clusters. When a result is obtained, the “local-search” mechanism launches several rounds of OCSVM each of which works with a new clustering configuration. Each new configuration is from the current configuration with incrementally modifications. The clustering results obtained from the local searches are post-evaluated by specific clustering validity index and the best one is retained. The clustering configuration of the best result is used by OCSVM for the clustering afterwards. Such a clustering process iterates until no better result can be obtained. This paper describes the clustering algorithm and compares three clustering validity indices, i.e. distance-based index, Davies–Bouldin index, and Xie–Beni index, on their effectiveness. The performance of the proposed method is demonstrated on the segmentation of several aerial images. Experimental results show that the proposed approach is feasible and effective for image segmentation.