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Incremental clustering of sonar images using self-organizing maps combined with fuzzy adaptive resonance theory

Abstract : In this paper we introduce a new unsupervised segmentation algorithm for textured sonar images. A Dynamic Self-Organizing Maps (DSOM) algorithm capable of incremental learning has been developed to automatically cluster the input data into relevant classes of seabed. DSOM algorithm is an extension of classical Self Organizing Maps (SOM) algorithm combined with Adaptive Resonance Theory (ART) technique. The proposed approach is based on growing map size during learning processes. Starting with a minimal number of neurons, the map size increases dynamically and the growth is controlled by the vigilance threshold of the ART network. To assess the consistency of the proposed approach, the DSOM algorithm is first tested on simulated data sets and then applied on real sidescan sonar images. The results obtained using the proposed approach demonstrate its capability to successfully cluster sonar images into their relevant seabed classes, very close to those resulting from human expert interpretation.
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https://hal-ensta-bretagne.archives-ouvertes.fr/hal-01699476
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Ahmed Nait Chabane, Naveed Islam, Benoit Zerr. Incremental clustering of sonar images using self-organizing maps combined with fuzzy adaptive resonance theory. Ocean Engineering, Elsevier, 2017, 142, pp.133-144. ⟨10.1016/j.oceaneng.2017.06.061⟩. ⟨hal-01699476⟩

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