Monday, May 25, 2015

Book: Dictionary Learning in Visual Computing

I received the following from Qiang recently:
Hi, Igor,

This is Qiang Zhang, a Ph.D. in Arizona State University and now staff research scientist in Samsung Electronics.

I have been reading your Nuit Blanche since the very beginning of my Ph.D. life in 2009. It has given me a lot of help in my study and research in sparse learning.

I'd like to introduce my new book "Dictionary Learning in Visual Computing", which describes the recent advances (2008~2014) in dictionary learning, specialized in computer vision. The book covers both algorithms and applications, including a detailed example of how to use dictionary learning in face recognition tasks.

The abstract: The last few years have witnessed fast development on dictionary learning approaches for a set of visual computing tasks, largely due to their utilization in developing new techniques based on sparse representation. Compared with conventional techniques employing manually defined dictionaries, such as Fourier Transform and Wavelet Transform, dictionary learning aims at obtaining a dictionary adaptively from the data so as to support optimal sparse representation of the data. In contrast to conventional clustering algorithms like K-means, where a data point is associated with only one cluster center, in a dictionary-based representation, a data point can be associated with a small set of dictionary atoms. Thus, dictionary learning provides a more flexible representation of data and may have the potential to capture more relevant features from the original feature space of the data. One of the early algorithms for dictionary learning is K-SVD. In recent years, many variations/extensions of K-SVD and other new algorithms have been proposed, with some aiming at adding discriminative capability to the dictionary, and some attempting to model the relationship of multiple dictionaries. One prominent application of dictionary learning is in the general field of visual computing, where long-standing challenges have seen promising new solutions based on sparse representation with learned dictionaries. With a timely review of recent advances of dictionary learning in visual computing, covering the most recent literature with an emphasis on papers after 2008, this book provides a systematic presentation of the general methodologies, specific algorithms, and examples of applications for those who wish to have a quick start on this subject.

The link to the publisher is


Qiang(Charles) Zhang
CSE, Arizona State University
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