BackgroundPlankton, including phytoplankton and zooplankton, are the main source of food for organisms in the ocean and form the base of marine food chain. As the fundamental components of marine ecosystems, plankton is very sensitive to environment changes, and the study of plankton abundance and distribution is crucial, in order to understand environment changes and protect marine ecosystems. This study was carried out to develop an extensive applicable plankton classification system with high accuracy for the increasing number of various imaging devices. Literature shows that most plankton image classification systems were limited to only one specific imaging device and a relatively narrow taxonomic scope. The real practical system for automatic plankton classification is even non-existent and this study is partly to fill this gap.ResultsInspired by the analysis of literature and development of technology, we focused on the requirements of practical application and proposed an automatic system for plankton image classification combining multiple view features via multiple kernel learning (MKL). For one thing, in order to describe the biomorphic characteristics of plankton more completely and comprehensively, we combined general features with robust features, especially by adding features like Inner-Distance Shape Context for morphological representation. For another, we divided all the features into different types from multiple views and feed them to multiple classifiers instead of only one by combining different kernel matrices computed from different types of features optimally via multiple kernel learning. Moreover, we also applied feature selection method to choose the optimal feature subsets from redundant features for satisfying different datasets from different imaging devices. We implemented our proposed classification system on three different datasets across more than 20 categories from phytoplankton to zooplankton. The experimental results validated that our system outperforms state-of-the-art plankton image classification systems in terms of accuracy and robustness.ConclusionsThis study demonstrated automatic plankton image classification system combining multiple view features using multiple kernel learning. The results indicated that multiple view features combined by NLMKL using three kernel functions (linear, polynomial and Gaussian kernel functions) can describe and use information of features better so that achieve a higher classification accuracy.

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The Southeast Asia and Western Pacific regions contain half of the world's children and are among the most rapidly industrializing regions of the globe. Environmental threats to children's health are widespread and are multiplying as nations in the area undergo industrial development and pass through the epidemiologic transition. These environmental hazards range from traditional threats such as bacterial contamination of drinking water and wood smoke in poorly ventilated dwellings to more recently introduced chemical threats such as asbestos construction materials; arsenic in groundwater; methyl isocyanate in Bhopal, India; untreated manufacturing wastes released to landfills; chlorinated hydrocarbon and organophosphorous pesticides; and atmospheric lead emissions from the combustion of leaded gasoline. To address these problems, pediatricians, environmental health scientists, and public health workers throughout Southeast Asia and the Western Pacific have begun to build local and national research and prevention programs in children's environmental health. Successes have been achieved as a result of these efforts: A cost-effective system for producing safe drinking water at the village level has been devised in India; many nations have launched aggressive antismoking campaigns; and Thailand, the Philippines, India, and Pakistan have all begun to reduce their use of lead in gasoline, with resultant declines in children's blood lead levels. The International Conference on Environmental Threats to the Health of Children, held in Bangkok, Thailand, in March 2002, brought together more than 300 representatives from 35 countries and organizations to increase awareness on environmental health hazards affecting children in these regions and throughout the world. The conference, a direct result of the Environmental Threats to the Health of Children meeting held in Manila in April 2000, provided participants with the latest scientific data on children's vulnerability to environmental hazards and models for future policy and public health discussions on ways to improve children's health. The Bangkok Statement, a pledge resulting from the conference proceedings, is an important first step in creating a global alliance committed to developing active and innovative national and international networks to promote and protect children's environmental health.