Using multiple uncertain examples and adaptative fuzzy reasoning to optimize image characterization

This article proposes an automatic characterization method by comparing unknown images with examples more or less known. Our approach allows to use uncertain examples but easy to obtain (e.g. by automatic retrieval on the Internet). The use of fuzzy logic and adaptive clustering makes it possible to reduce automatically the noise from this database by preserving only the examples having a strong level of redundancy in the dominant shapes. To validate this method, we compared our artificial process of recognition with the estimation of human operators. The tests show that the automatic process gives an average accuracy of the characterization near to 95%.