In this work a novel technique for representing the edges of an image is presented and the impact of this on image clustering is investigated. The characterization is performed in two steps: the “most important” edges are first selected by using both the Laplace operator and the Laguerre Gauss functions, and then the phase distribution of each edge point is estimated. The similarity is measured by using the Euclidean distance. The query-by-example systems usually rank in the first positions objects very similar to the query. If many almost identical copies of the query object are present in the database, all of them are shown. However, some object that are interesting are not ranked in the first positions. To this aim a clustering method is used. This method is based on the low level features, such as edges, texture, and color.
['Luca Costantini', 'Licia Capodiferro', 'Marco Carli', 'Alessandro Neri']
Impact of edges characterization on image clustering