Studying the characteristics of image and video data can lead to a higher
understanding of the environment and offer a natural interface between users
and their surroundings. However, the massive amounts of data and the
associated complexity encumber the transfer of sophisticated vision
algorithms to real life systems. One approach for addressing these issues is
to generate compact and descriptive representations of image data. In this
talk, we investigate dimensionality reduction and sparse representations to
accomplish this task. More specifically, the application of nonlinear
dimensionality reduction techniques and sparse coding are investigated in
three hierarchical image layers, namely low-level features, mid-level
structures and high-level attributes. For the low-level features, various
techniques for dimensionality reduction, ranging from traditional image
compression to the recently proposed Random Projections method, are
explored. The application of these methods on computer vision algorithms,
such as face detection and face recognition, are analyzed. In addition, a
novel approach to super resolution is presented that is capable of
increasing the spatial resolution of a single image based on the sparse
representations framework.


In the second part, mid-level structures,
including image manifolds and sparse models, are utilized for face
recognition and object tracking. A new method for robust object tracking
under appearance and illumination variations is presented based on a
combination of a template library, online distance metric learning, and the
Random Projections transformation.


In the third part, a novel framework for representing the semantic contents
of images is investigated. This framework employs high level semantic
attributes that aim to bridge the gap between the visual information of an
image and its textual description by utilizing low level features and mid
level structures. This innovative paradigm offers revolutionary
possibilities including recognizing the category of an object from purely
textual information without providing any explicit visual example.