Shape Retrieval by Partially Supervised Fuzzy Clustering

In this work we propose the use of partially supervised fuzzy clustering to create a two-level indexing structure useful for enabling efficient shape retrieval. Similar shapes are grouped by a fuzzy clustering algorithm that embeds a partial supervision mechanism exploiting domain knowledge expressed in terms of a set of labeled shapes. After clustering, a set of prototypes representative of shape clusters is derived and used as indexing mechanism for retrieval. A shape query is matched against prototypes, instead of the whole shape database, and then shapes belonging to clusters for which prototype similarity is higher are returned. Experimental results obtained on two different datasets are presented to show the effectiveness of the proposed approach.