  In big data image/video analytics, we encounter the problem of learning an
overcomplete dictionary for sparse representation from a large training
dataset, which can not be processed at once because of storage and
computational constraints. To tackle the problem of dictionary learning in such
scenarios, we propose an algorithm for parallel dictionary learning. The
fundamental idea behind the algorithm is to learn a sparse representation in
two phases. In the first phase, the whole training dataset is partitioned into
small non-overlapping subsets, and a dictionary is trained independently on
each small database. In the second phase, the dictionaries are merged to form a
global dictionary. We show that the proposed algorithm is efficient in its
usage of memory and computational complexity, and performs on par with the
standard learning strategy operating on the entire data at a time. As an
application, we consider the problem of image denoising. We present a
comparative analysis of our algorithm with the standard learning techniques,
that use the entire database at a time, in terms of training and denoising
performance. We observe that the split-and-merge algorithm results in a
remarkable reduction of training time, without significantly affecting the
denoising performance.
