Segmentation Ensemble - A Knowledge Reuse for Model Order Selection using Case-based Reasoning

Abstract

Cluster ensemble has emerged as a powerful technique for improving robustness, stability, and accuracy of clustering solutions, however, automatic estimating the appropriate number of clusters in the final combined results remains unsolved. In this paper we present a new approach based on a case-based reasoning to handle this difficult task. The key success of our approach is a novel use of cluster ensemble in a different role from the past. Each ensemble component is viewed as an expert domain for building a case base. Having benefited from the information extracted from cluster ensemble, a case-based reasoning is able to settle efficiently the appropriate number of clusters underlying a clustering ensemble. Our approach is simple, fast and effective. Three simulations with different state-of-the-art segmentation algorithms are presented to illustrate the efficacy of the proposed approach. We extensively evaluate our approach on a large dataset in comparison with recent approaches for determining the number of regions in segmentation combination framework. Experiments demonstrate that our approach can substantially reduce computational time required by the existing methods, more importantly, without the loss of segmentation combination accuracy. This contribution makes the segmentation ensemble combination concept more feasible in real-world applications.