John Gallagher (Committee Member), Frederick Garber (Committee Member), Michael Raymer (Committee Member), Mateen Rizki (Advisor), Vincent Velten (Committee Member)
Doctor of Philosophy (PhD)
Transfer Subspace Learning has recently gained popularity for its ability to perform cross-dataset and cross-domain object recognition. The ability to leverage existing data without the need for additional data collections is attractive for Aided Target Recognition applications. For Aided Target Recognition (or object assessment) applications, Transfer Subspace Learning is particularly useful, as it enables the incorporation of sparse and dynamically collected data into existing systems that utilize large databases. In this dissertation, Manifold Learning and Transfer Subspace Learning are combined to create new Aided Target Recognition systems capable of achieving high target recognition rates for cross-dataset conditions and cross-domain applications. The Manifold Learning technique used in this dissertation is Diffusion Maps, a nonlinear dimensionality reduction technique based on a heat diffusion analogy. The Transfer Subspace Learning technique used is Transfer Fishers Linear Discriminative Analysis. The new Aided Target Recognition systems introduced in this dissertation are (i) Manifold Transfer Subspace Learning, which combines Manifold Learning and Transfer Subspace Learning sequentially, and (ii) Transfer Diffusion Maps, which simultaneously integrates Manifold Learning and Transfer Subspace Learning. Finally, the ability of the new techniques to achieve high target recognition rates for cross-dataset and cross-domain applications is illustrated using a variety of diverse datasets.
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
Copyright 2017, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.