Graduation Year

2005

Document Type

Thesis

Degree

M.S.C.S.

Degree Granting Department

Computer Science

Major Professor

Dr. Dmitry Goldgof.

Keywords

SIPPER, Feature selection, Feature calculation, Active learning, Support vector machine, SVM, Multi-class

Abstract

Utilizing a continuous silhouette image of marine plankton produced by a device called SIPPER, developed by the Marine Sciences Department, individual plankton images were extracted, features were derived, and classification was performed. There were plankton recognition experiments performed in Support Vector Machine parameter tuning, Fourier descriptors, and feature selection. Several groups of features were implemented, moments, gramulometric, Fourier transform for texture, intensity histograms, Fourier descriptors for contour, convex hull, and Eigen ratio. The Fourier descriptors were implemented in three different flavors sampling, averaging and hybrid (mix of sampling and averaging). The feature selection experiments utilized a modified WRAPPER approach of which several flavors were explored including Best Case Next, Forward and Backward, and Beam Search.

Feature selection significantly reduced the number of features required for processing, while at the same time maintaining the same level of classification accuracy. This resulted in reduced processing time for training and classification.

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