Classification via Dimensionality Reduction for Computer Vision Applications

Abstract

We investigate several computer vision problems using classification techniques based on dimensionality reduction. Our methods are derived from manifold-learning viewpoints, and have many practical advantages for addressing vision applications such as face/object recognition, real-time visual tracking, and image retrieval. The presentation will include the key ideas of our approaches as well as comprehensive examples to demonstrate the efficiencies of the proposed algorithms.