ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

A Split-and-Merge Dictionary Learning Algorithm for Sparse Representation: Application to Image Denoising

Mukherjee, Subhadip and Seelamantula, Chandra Sekhar (2014) A Split-and-Merge Dictionary Learning Algorithm for Sparse Representation: Application to Image Denoising. In: 19th International Conference on Digital Signal Processing (DSP), AUG 20-23, 2014, Hong Kong, PEOPLES R CHINA, pp. 310-315.

[img] PDF
2014-19th_Int_Con_on_Dig_Sig_Pro_310_2014.pdf - Published Version
Restricted to Registered users only

Download (1MB) | Request a copy
Official URL: http://arxiv.org/abs/1403.4781


In big data image/video analytics, we encounter the problem of learning an over-complete dictionary for sparse representation from a large training dataset, which cannot be processed at once because of storage and computational constraints. To tackle the problem of dictionary learning in such scenarios, we propose an algorithm that exploits the inherent clustered structure of the training data and make use of a divide-and-conquer approach. The fundamental idea behind the algorithm is to partition the training dataset into smaller clusters, and learn local dictionaries for each cluster. Subsequently, the local dictionaries are merged to form a global dictionary. Merging is done by solving another dictionary learning problem on the atoms of the locally trained dictionaries. This algorithm is referred to as the split-and-merge algorithm. 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, which operates 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.

Item Type: Conference Proceedings
Series.: International Conference on Digital Signal Processing
Publisher: IEEE
Additional Information: Copy right for this article belongs to the IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Keywords: Dictionary learning; Parallel learning; Split-and-merge; Sparsity; Image denoising; Big data analytics
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 09 Oct 2015 05:48
Last Modified: 09 Oct 2015 05:48
URI: http://eprints.iisc.ac.in/id/eprint/52526

Actions (login required)

View Item View Item