A stochastic framework for K-SVD with applications on face recognition

In recent years, the sparse representation modeling of signals has received a lot of attention due to its state-of-the-art performance in different computer vision tasks. One important factor to its success is the ability to promote representations that are well adapted to the data. This is achieved...

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Bibliographic Details
Published in:Pattern analysis and applications : PAA 2017-08, Vol.20 (3), p.845-854
Main Authors: Malkomes, Gustavo, de Brito, Carlos Eduardo Fisch, Gomes, João Paulo Pordeus
Format: Article
Language:English
Subjects:
Adaptation
Algorithms
Computer Science
Computer vision
Dictionaries
Face recognition
Facial recognition technology
Greedy algorithms
Machine learning
Pattern Recognition
Probability theory
Randomness
Representations
Singular value decomposition
Theoretical Advances
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Summary:In recent years, the sparse representation modeling of signals has received a lot of attention due to its state-of-the-art performance in different computer vision tasks. One important factor to its success is the ability to promote representations that are well adapted to the data. This is achieved by the use of dictionary learning algorithms. The most well known of these algorithms is K-SVD. In this paper, we propose a stochastic framework for K-SVD called α K-SVD. The α K-SVD uses a parameter α to control a compromise between exploring the space of dictionaries and improving a possible solution. The use of this heuristic search strategy was motivated by the fact that K-SVD uses a greedy search algorithm with fast convergence, possibly leading to local minimum. Our approach is evaluated on two public face recognition databases. The results show that our approach yields better results than K-SVD and LC-KSVD (a K-SVD adaptation to classification) when the sparsity level is low.
ISSN:1433-7541
1433-755X
DOI:10.1007/s10044-016-0541-3