Paper Info
Title
Class Relatedness Oriented-Discriminative Dictionary Learning for Multiclass Image Classification
Abstract
Dictionary learning (DL) has recently attracted intensive attention due to its representative and discriminative power in various classification tasks. Although much progress has been reported in the existing supervised DL approaches, it is still an open problem that how to build the relationship between dictionary atoms and the class labels in multiclass classification. In this paper, based on the assumption that the relevance of dictionary atoms could be helpful in multiclass classification task, we proposed a class relatedness oriented (CRO) discriminative dictionary learning method for sparse coding. Utilizing the ź 1 , ∞ -norm regularization on the coding coefficient matrix, the proposed method can adaptively learn the class relatedness between dictionary atoms and the multiclass labels. Experimental results of face recognition, object classification, and action recognition demonstrate that our proposed method is comparable to many state-of-the-art DDL methods. HighlightsWe propose a new discriminative dictionary learning method for image classification.We show that the proposed method can adaptively learn the relatedness between dictionary atoms and the class labels.The proposed method achieves state-of-the-art performance on several public databases.
Year
DOI
Venue
2016
10.1016/j.patcog.2015.12.005
Pattern Recognition
Keywords
Field
DocType
support vector machine
Facial recognition system,Pattern recognition,K-SVD,Computer science,Neural coding,Support vector machine,Coding (social sciences),Artificial intelligence,Contextual image classification,Discriminative model,Machine learning,Multiclass classification
Journal
Volume
Issue
ISSN
59
C
0031-3203
Citations 
PageRank 
References 
5
0.39
28
Authors
4
Name
Order
Citations
PageRank
Dongyu Zhang115123.10
pengju liu2292.38
Kai Zhang368626.59
Hongzhi Zhang412219.79