ABSTRACT OF THE DISSERTATION Selection-based Dictionary Learning for Sparse Representation in Visual Tracking

This dissertation describes a novel selection-based dictionary learning method with a sparse representation to tackle the object tracking problem in computer vision. The sparse representa-tion has been widely used in many applications including visual tracking, compressive sensing, image de-noising and image classification, and learning a good dictionary for the sparse rep-resentation is critical for obtaining high performance. The most popular existing dictionary learning algorithms are generalized from K-means, which compute the dictionary columns to minimize the overall target reconstruction error iteratively. For better discriminative capability to differentiate target-object (positive) from background (negative) data, a class of dictionary algorithms has been developed to learn the dictionary from both the positive and the negative data. However, these methods do not work well for visual tracking in a dynamic environment in which the background can change considerably between frames in a non-linear way. The background cannot be modeled statically with the usual linear models. In this dissertation, I report on the development of a selection-based dictionary learning algorithm (K-Selection) that constructs the dictionary by choosing its columns from the training data. Each column is the most representative basis for the whole dataset, which also has a clear physical meaning. Wit