Classification and retrieval on macroinvertebrate image databases

Aquatic ecosystems are continuously threatened by a growing number of human induced changes. 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-intensive 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, in this paper we shall investigate the feasibility of automated river macroinvertebrate classification and retrieval with high precision. Besides the state-of-the-art classifiers such as Support Vector Machines (SVMs) and Bayesian Classifiers (BCs), the focus is particularly drawn on feed-forward artificial neural networks (ANNs), namely multilayer perceptrons (MLPs) and radial basis function networks (RBFNs). Since both ANN types have been proclaimed superior by different investigations even for the same benchmark problems, we shall first show that the main reason for this ambiguity lies in the static and rather poor comparison methodologies applied in most earlier works. Especially the most common drawback occurs due to the limited evaluation of the ANN performances over just one or few network architecture(s). Therefore, in this study, an extensive evaluation of each classifier performance over an ANN architecture space is performed. The best classifier among all, 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 high accuracy and can match an experts' ability for taxonomic identification.