Abstract: Correlations among elements in a video exist in different regions both spatially
within a frame and temporally through frames in a video. Conventional tensor-based
action classification algorithms do not fully exploit the correlations in a video.
In this thesis, we study the problem of how to rearrange elements in a video for
achieving better classification accuracy. The element rearrangement problem is
developed in the context of employing element rearrangement as a preprocessing step
for the action classification on product manifolds method. The problem is
formulated as a combinatorial optimization problem, and local search methods are
presented for exploiting the correlations in a video tensor. In particular, tabu
search, a simple and useful metaheuristic, is adopted in this work to search for
the best rearrangement of elements in a video tensor. Several neighborhood
structures and search strategies are explored. We assess the proposed methods using
a publicly available video data set, namely Cambridge-Gesture data set.
Experimental results are reported on different tabu search heuristics. Results
reveal that the proposed element rearrangement algorithm improves the
classification accuracy of the action classification on product manifolds method.

Adviser: J. Ross Beveridge
Co-Adviser: N/A
Non-ECE Member: peterson@math.colostate.edu
Member 3: aam@colostate.edu
Addional Members: sanjay.rajopadhye@colostate.edu

Publications:
N/A

Program of Study:
Advanced Topics in Computer Vision
Topics in Robotics
Digital Image Processing
Optimization Methods-Control and Communication
Fundamentals of High Performance Computing
Foundations of Fine-Grain Parallelism
Robot Motion Planning
Digital Control and Digital Filters