A Fuzzy Set Approach for Shape-Based Image Annotation

In this paper, we present a shape labeling approach for automatic image annotation. A fuzzy clustering process is applied to shapes represented by Fourier descriptors in order to derive a set of shape prototypes. Then, prototypes are manually annotated by textual labels corresponding to semantic categories. Based on the labeled prototypes, a new shape is automatically labeled by associating a fuzzy set that provides membership degrees of the shape to all semantic classes. Preliminary results show the suitability of the proposed approach to image annotation by encouraging its application in wider application contexts.