Experts Fusion and Multilayer Perceptron Based on Belief Learning for Sonar Image Classification

Abstract

The sonar images provide a rapid view of the seabed in order to characterize it. However, in such as uncertain environment, real seabed is unknown and the only information we can obtain, is the interpretation of different human experts, sometimes in conflict. In this paper, we propose to manage this conflict in order to provide a robust reality for the learning step of classification algorithms. The classification is conducted by a multilayer perceptron, taking into account the uncertainty of the reality in the learning stage. The results of this seabed characterization are presented on real sonar images.

Support vector regression of membership functions and belief functions – Application for pattern recognition

“…Many classifiers were developed in the framework of these theories such as fuzzy neural networks [18], belief neural networks [21,25], belief k-nearest neighbors [22,34], fuzzy k-nearest neighbors [19,20] or belief decision trees [23,24].…”

“…For pattern recognition, many methods were developed within the framework of the theories of uncertainty using existing methods such as neural networks, k-nearest neighbors or decision trees, giving new approaches for classification such as fuzzy classifiers [18][19][20] or belief classifiers [21][22][23][24][25].…”