Image understanding has been playing an increasingly crucial role in several inverse problems
and computer vision. Sparse models form an important component in image understanding since
they emulate the activity of neural receptors in the primary visual cortex of the human brain.
Sparse methods have been utilized in several learning problems because of their ability to
provide parsimonious interpretable and efficient models. Exploiting the sparsity of natural
signals has led to advances in several application areas including image compression denoising
inpainting compressed sensing blind source separation super-resolution and classification.
The primary goal of this book is to present the theory and algorithmic considerations in using
sparse models for image understanding and computer vision applications. To this end algorithms
for obtaining sparse representations and their performance guarantees are discussed in the
initial chapters. Furthermore approaches for designing overcomplete data-adapted dictionaries
to model natural images are described. The development of theory behind dictionary learning
involves exploring its connection to unsupervised clustering and analyzing its generalization
characteristics using principles from statistical learning theory. An exciting application area
that has benefited extensively from the theory of sparse representations is compressed sensing
of image and video data. Theory and algorithms pertinent to measurement design recovery and
model-based compressed sensing are presented. The paradigm of sparse models when suitably
integrated with powerful machine learning frameworks can lead to advances in computer vision
applications such as object recognition clustering segmentation and activity recognition.
Frameworks that enhance the performance of sparse models in such applications by imposing
constraints based on the prior discriminatory information and the underlying geometrical
structure and kernelizing the sparse coding and dictionary learning methods are presented. In
addition to presenting theoretical fundamentals in sparse learning this book provides a
platform for interested readers to explore the vastly growing application domains of sparse
representations.
