Ph.D. Dissertation Proposal

A Rapidly Deployable Image
Classification System Using Multi-Views

Adrian Rosebrock

11:00am Friday, 10 May, ITE 325, UMBC

Constructing an image classification system using strong, local invariant descriptors is time consuming and tedious, requiring many experimentations and parameter tuning to obtain an adequately performing model. Furthermore, training a system in a given domain and then migrating the model to a separate domain will likely yield poor performance. As computer vision systems become more prevalent in the academic, government, and private sectors, it is paramount that a framework to more easily construct these classification systems be created. In this work we present a rapidly deployable image classification system using multi-views, where each view consists of a set of weak global features. These weak global descriptors are computationally simple to extract, intuitive to understand, and require substantially less parameter tuning than their local invariant counterparts. We demonstrate that by combining weak features with ensemble methods we are able to outperform the current state-of-the-art methods or achieve comparable accuracy. Finally, we provide a theoretical justification for our ensemble framework that can be used to construct rapidly deployable image classification systems called "Ecosembles".

Committee: Dr. Tim Oates (chair), Dr. Jesus Caban, Dr. Tim Finin, Dr. Charles Nicholas