Sipper Project Page

Project Description

The PICES software package is an image classification and databse system intended for the fast identification of plankton images that were generated by the SIPPER device. These images are grayscale and can contain either 2 or 8 levels of intensity. Images of individule plankton with their their related instrumentation data are stored in a MySQL database where they can be quickly retrieved by supporting applications.

The goal of this project is to provide marine scientists (and others) with the ability to rapidly determine the plankton composition of a region of water. Normally this process would be a painstaking and tedious task, but the SIPPER software makes use of active learning techniques to aide the scientist in classifying thousands of plankton images in a relatively short period of time.

The SIPPER project is a collaboration of the University of South Florida's Computer Science and Engineering and Marine Science departments.


Papers

    Journal Publications

  • Kurt A. Kramer, Lawrence O. Hall, 2 Fellow, IEEE, Dmitry B. Goldgof, Andrew Remsen, and Tong Luo, "Fast Support Vector Machines for Continuous Data", IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 39(4), pp. 989-1001, March 2009. (PDF) (Data Sets)
  • T. Luo, K. Kramer, D. Goldgof, L. Hall, S. Samson, A. Remsen, T. Hopkins, "Active Learning to Recognize Multiple Types of Plankton", Journal of Machine Learning Research, JMLR 6(Apr): pp. 589-613, 2005.(PDF) (Data Sets)
  • T. Luo, K. Kramer, D. Goldgof, L. Hall, S. Samson, A. Remsen, T. Hopkins, "Recognizing Plankton Images from the Shadow Image Particle Profiling Evaluation Recorder", IEEE Transactions in System Man and Cybernetics, B, 34(4), pp. 1753-1762, August 2004. (PDF).
  • A. Remsen, S. Samson, T. Hopkins, "What you see is not what you catch: A comparison of concurrently collected net, optical plankton counter (OPC), and Shadowed Image Particle Profiling Evaluation Recorder (SIPPER) data from the northeast Gulf of Mexico" Deep Sea Research, I, 51(1), pp. 129-151, 2004.
  • S. Samson, T. Hopkins, A. Remsen, L. Langebrake, T. Sutton, J. Patten, "A system for high resolution zooplankton imaging" IEEE Journal of Oceanic Engineering 26(4), pp. 671-676, 2001.

    Conference Publications

  • K. Kramer, D. Goldgof, L. Hall, A. Remsen "Increased Classification Accuracy and Speedup Through Pair-wise Feature Selection for Support Vector Machines", IEEE Symposium Series on Computational Intelligence, pp. 318-324, Paris France, April 2011(PDF)(SIPPER Data Set)
  • T. Luo, L. Hall, D. Goldgof, A. Remsen "Bit Reduction Support Vector Machine", Data Mining, Fifth IEEE International Conference on Data Mining, ISBN: 0-7695-2278-5, pp. 3451-3456, November 2005(PDF)
  • T. Lou, K. Kramer, D. Goldgof, L. Hall, S. Sampson, A. Remsen, T. Hopkins, "Active Learning to Recognize Multiple Types of Plankton", International Conference on Pattern Recognition (ICPR), Cambridge, UK, August 2004. (preprint PDF)
  • T. Lou, K. Kramer, D. Goldgof, L. Hall, S. Sampson, A. Remsen, T. Hopkins, "Learning to Recognize Plankton", IEEE International Conference on Systems, Man, and Cybernetics, Washington, D.C., pp. 888-893, October 2003 (PDF)