Uncertain clustering algorithms based on rough and fuzzy sets for real-time image segmentation

In real pattern recognition applications, the complete and accurate information of a given set is not always easy to get. Such incomplete knowledge may lead to imperfect expressions of the set using many pattern recognition methods. Rough sets theory is designed to approximately describe an imprecise set by a pair of lower and upper approximations which are weighted by different parameters. As the distributive character varies from one set to another, it is undesirable to employ a constant weighted parameter for controlling the importance of the lower and upper approximations on describing various given sets. This paper presents an improved rough-fuzzy c-means clustering algorithm in which a parameter selection strategy is designed to adaptively adjust the weighted parameter depending on the distributive character of each cluster instead of manually choosing a constant parameter. Such an online-decision method enables the formed prototype to get close to the desirable location. Experimental results on synthetic datasets, real-life datasets, and image segmentation problems confirm the effectiveness of the proposed adaptive parameter selection strategy. With the introduction of adaptive parameter selection strategy, the improved rough sets-based clustering algorithm outperforms its counterparts in certain cases.