Shape annotation by incremental semi-supervised fuzzy clustering

Automatic image annotation is an important and challenging task when managing large image collections. In this paper, we present an incremental approach for shape labeling, which is useful to image annotation when new sets of images are available during time. Every time new shape images are available, a semi-supervised fuzzy clustering algorithm is used to group shapes into a number of clusters by exploiting knowledge about classes expressed as a set of pre-labeled shapes. Each cluster is represented by a prototype that is manually labeled and used to annotate shapes. To capture the evolution of the image set, the previously discovered prototypes are added as pre-labeled shapes to the current shape set before clustering. The performance of the proposed incremental approach is evaluated on an image dataset from the fish domain, which is divided into chunks of data to simulate the progressive availability of shapes during time.