Classification and Retrieval on Macroinvertabrate Image Databases Using Evolutionary Rbf Neural Networks

Aquatic ecosystems are facing a growing number of human induced changes and threats. Macroinvertebrate biomonitoring is particularly efficient in pinpointing the cause-effect structure between slow and subtle changes and their detrimental consequences in aquatic ecosystems. The greatest obstacle to implementing efficient biomonitoring is currently the cost-intensity of human expert taxonomic identification of samples. While there is evidence that automated recognition techniques can match human taxa identification accuracy at greatly reduced costs, so far the development of automated identification techniques for aquatic organisms has been minimal. In this paper, we focus on advancing classification and data retrieval that are instrumental when processing large macroinvertebrate image datasets. To accomplish this for routine biomonitoring we propose an automated and highly accurate river macroinvertebrate classifier using evolutionary RBF networks. The best classifier, which is trained over a dataset of river macroinvertebrate specimens, is then used in the MUVIS framework for the efficient search and retrieval of particular macroinvertebrate peculiars. Classification and retrieval results present such a delicate accuracy that can match experts’ ability for taxonomic identification.