In this paper we propose a symbolic classification system able to solve automatically a great number of different image classification problems, without any need to adapt the preprocessing procedure to the specific problem instance at hand. The basic idea consists in considering a set of semantically defined objects (symbolic elements) that can be recognized on images. By means of a segmentation procedure, each image is represented by a set of symbolic elements. The inductive inference is performed directly in this symbolic domain through a parametric dissimilarity measure. As shown in this paper, the system is able to adapt the dissimilarity measure to the specific problem, by finding the optimal values of the dissimilarity function parameters. Moreover, a compact model representation can be obtained by representing each cluster with the corresponding set median point. © 2005 IEEE.
A Symbolic Approach to the Solution of F-Classification Problems / RIZZI, Antonello; DEL VESCOVO, Guido. - STAMPA. - 3:(2005), pp. 1953-1958. (Intervento presentato al convegno IEEE International Joint Conference on Neural Networks (IJCNN 2005) tenutosi a Montreal; Canada nel 31 July-4 Aug. 2005) [10.1109/IJCNN.2005.1556179].
A Symbolic Approach to the Solution of F-Classification Problems
RIZZI, Antonello;DEL VESCOVO, Guido
2005
Abstract
In this paper we propose a symbolic classification system able to solve automatically a great number of different image classification problems, without any need to adapt the preprocessing procedure to the specific problem instance at hand. The basic idea consists in considering a set of semantically defined objects (symbolic elements) that can be recognized on images. By means of a segmentation procedure, each image is represented by a set of symbolic elements. The inductive inference is performed directly in this symbolic domain through a parametric dissimilarity measure. As shown in this paper, the system is able to adapt the dissimilarity measure to the specific problem, by finding the optimal values of the dissimilarity function parameters. Moreover, a compact model representation can be obtained by representing each cluster with the corresponding set median point. © 2005 IEEE.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.