Analysis K-SVD: A Dictionary-Learning Algorithm for the Analysis Sparse Model

By Ron Rubinstein, Tomer Peleg, Michael Elad. IEEE Transactions on Signal Processing, 2013

476 citations recorded by the source corpus.

The synthesis-based sparse representation model for signals has drawn considerable interest in the past decade. Such a model assumes that the signal of interest can be decomposed as a linear combination of a few atoms from a given dictionary. In this paper we concentrate on an alternative, analysis-based model, where an analysis operator-hereafter referred to as the analysis dictionary-multiplies the signal, leading to a sparse outcome. Our goal is to learn the analysis dictionary from a set of examples. The approach taken is parallel and similar to the one adopted by the K-SVD algorithm that serves the corresponding problem in the synthesis model. We present the development of the algorithm steps: This includes tailored pursuit algorithms-the Backward Greedy and the Optimized Backward Greedy algorithms, and a penalty function that defines the objective for the dictionary update stage. We demonstrate the effectiveness of the proposed dictionary learning in several experiments, treating synthetic data and real images, and showing a successful and meaningful recovery of the analysis dictionary.

Subfields: Biomedical Engineering, Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/15dbde3eedc561c53dbccf15f9cc9204d8e996a9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Xiaojie Guo

In Computational Mechanics

1. Xiaochun Cao
2. Xiaojie Guo

In Computer Vision and Pattern Recognition

1. Ling Du
2. Dan Meng
3. Xiaochun Cao
4. Xingxing Wei
5. Xiaojie Guo
6. Ling Shao
7. Wenhan Yang
8. Vishal M. Patel

Citations in the corpus

• High Capacity Reversible Data Hiding in Encrypted Images by Patch-Level Sparse Representation (2016, methodology): “1) Sparse Representation: For reserving room to hide data, we train the dictionary based on K-means singular value decomposition (K-SVD) algorithm [33], [34], which is widely used for designing over-complete dictionaries that lead to sparse signal representation.”