Title :
A symbolic approach to the solution of F-classification problems
Author :
Rizzi, Antonello ; Del Vescovo, Guido
Author_Institution :
Dept. of INFOCOM, Rome La Sapienza Univ., Italy
fDate :
31 July-4 Aug. 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.
Keywords :
image classification; image representation; image segmentation; inference mechanisms; F-classification; image classification; image representation; image segmentation; inductive inference; parametric dissimilarity measure; set median point; Computational intelligence; Data structures; Electronic mail; IEEE members; Image classification; Image recognition; Image segmentation; Performance evaluation; Sociotechnical systems; Testing;
Conference_Titel :
Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on
Print_ISBN :
0-7803-9048-2
DOI :
10.1109/IJCNN.2005.1556179
