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VISUALIZATION THROUGH SELF-ORGANIZING MAPS : A CASE STUDY IN UNDERGRADUATE RESEARCH

1 James Wolfer, Department of Computer Science, Indiana University South Bend, 931, South Bend, IN, U.S.A., jwolfer@iusb.edu 2 Jacob Ratkiewicz, Department of Computer Science, Indiana University Bloomington, Bloomington, IN, U.S.A., jpr@cs.indiana.edu Abstract  The relative inexperience of typical undergraduate students coupled with the demands of graduate students often limits significant research opportunities for undergraduates. Indiana University South Bend has implemented a variety of programs that, collectively, provide pathways from proposal and grant application through publication. Here we feature a project involving the visualization of image texture descriptors as a case study for this process. Visual texture descriptors encode information about the underlying image for typically repetitive patterns such as textiles, wood grain, and masonry. Using Kohonen's self-organizing map for unsupervised cluster visualization, regions sensitive to similar textures are produced, providing insight into their discriminating potential. In this paper we use this undergraduate project as a foundation to describe the programs, process, infrastructure, results, and publication, along with a critique of the strengths and weaknesses of the undergraduate research program.