In this paper we propose an image classification system able to solve automatically a large set of problem instances by a granular computing approach. By means of a watershed segmentation algorithm, each image is decomposed into a set of segments (information granules), characterized by suited color, texture and shape features (segment signature). Successively, images are represented by a symbolic graph, where each node stores the segment signature and edges retain the information about the mutual spatial relations between segments. The induction engine is based on a parametric dissimilarity measure between graphs. A heuristic search procedure based on a genetic algorithm is able to find automatically both the segmentation parameters and the dissimilarity measure parameters, and hence the relevant features to the classification problem at hand. System performances have been measured on the basis of an image classification problem repository which has been specifically created to this aim. © 2006 IEEE.

Automatic Image Classification by a Granular Computing Approach / Rizzi, Antonello; DEL VESCOVO, Guido. - STAMPA. - (2006), pp. 33-38. ((Intervento presentato al convegno IEEE Workshop on Machine Learning for Signal Processing (MLSP 2006) tenutosi a Maynooth; Ireland nel 6-8 Settembre, 2006 [10.1109/MLSP.2006.275517].

Automatic Image Classification by a Granular Computing Approach

RIZZI, Antonello;DEL VESCOVO, Guido
2006

Abstract

In this paper we propose an image classification system able to solve automatically a large set of problem instances by a granular computing approach. By means of a watershed segmentation algorithm, each image is decomposed into a set of segments (information granules), characterized by suited color, texture and shape features (segment signature). Successively, images are represented by a symbolic graph, where each node stores the segment signature and edges retain the information about the mutual spatial relations between segments. The induction engine is based on a parametric dissimilarity measure between graphs. A heuristic search procedure based on a genetic algorithm is able to find automatically both the segmentation parameters and the dissimilarity measure parameters, and hence the relevant features to the classification problem at hand. System performances have been measured on the basis of an image classification problem repository which has been specifically created to this aim. © 2006 IEEE.
IEEE Workshop on Machine Learning for Signal Processing (MLSP 2006)
automatic image classification, granular computing, genetic algorithms, graph theory, image colour analysis, image segmentation, image texture, information granules, parametric dissimilarity measure, segment signature, shape features, watershed segmentation
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Automatic Image Classification by a Granular Computing Approach / Rizzi, Antonello; DEL VESCOVO, Guido. - STAMPA. - (2006), pp. 33-38. ((Intervento presentato al convegno IEEE Workshop on Machine Learning for Signal Processing (MLSP 2006) tenutosi a Maynooth; Ireland nel 6-8 Settembre, 2006 [10.1109/MLSP.2006.275517].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/62715
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