Application of image processing and machine learning techniques to distinguish suspected oil droplets from plankton and other particles for the SIPPER imaging system

Image classification features and examples of statistical results for the data mining approach using a one-versus-one strategy to implement a SVM (support vector machine) multi-class classifier. Data published in: Fefilatyev, S., K. Kramer, L. Hall, D. Goldgof, R. Kasturi, A. Remsen, K. Daly. 2011. Detection of Anomalous Particles from the Deepwater Horizon Oil Spill Using the SIPPER3 Underwater Imaging Platform. Proceedings of International Conference on Data Mining Workshops, p. 741-748. Awarded Data Mining Practice Prize at the IEEE International Conference on Data Mining (ICDM), Vancouver, Canada, December 11-14, 2011. DOI 10.1109/ICDMW.2011.65.

本数据集包含采用一对一（one-versus-one）策略实现支持向量机（support vector machine, SVM）多分类器的数据挖掘方法的图像分类特征与统计结果示例。相关数据发表于：Fefilatyev S、Kramer K、Hall L、Goldgof D、Kasturi R、Remsen A、Daly K于2011年发表的《使用SIPPER3水下成像平台检测深水地平线漏油事件中的异常颗粒》，收录于2011年12月11日至14日在加拿大温哥华举办的IEEE国际数据挖掘会议（ICDM）研讨会论文集，页码范围为741至748，该研究荣获本届IEEE国际数据挖掘会议的数据挖掘实践奖，DOI编号：10.1109/ICDMW.2011.65。