Incremental semi-supervised fuzzy clustering for shape annotation

Abstract

In this paper, we present an incremental clustering approach for shape annotation, which is useful when new sets of images are available over time. A semi-supervised fuzzy clustering algorithm is used to group shapes into a number of clusters. Each cluster is represented by a prototype that is manually labeled and used to annotate shapes belonging to that cluster. To capture the evolution of the image set over time, the previously discovered prototypes are added as pre-labeled objects to the current shape set and semi-supervised clustering is applied again. The proposed incremental approach is evaluated on two benchmark image datasets, which are divided into chunks of data to simulate the progressive availability of images during time.