.png){kind=icon}
.png){kind=icon}
Return
Double Graph Regularized Double Dictionary Learning for Image Classification
.png){kind=icon}
PRE
AI
DOI：10.1109/TIP.2020.3004246.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, we present a novel double graph regularized double dictionary learning (DGRDDL) method for image classification. The proposed method jointly constructs a number of class-specific sub-dictionaries to capture the most discriminative features (class-specific information) of each class, and a class-shared dictionary to model the common patterns (class-shared information) shared by the images from different classes. A novel double graph regularization is proposed to correctly represent and differentiate these two types of information. Specifically, an intra-class similarity graph constraint is imposed on the representation coefficients over the class-specific dictionaries, and an inter-class similarity graph constraint is applied on the representation coefficients over the class-shared dictionary. In this way, the representations learned by the proposed DGRDDL method can correctly model the local similarity relationships of the class-specific and the class-shared information in images, respectively. Moreover, due to the differences between the intra-class and inter-class similarity graphs, the two types of information can be appropriately separated and captured by the learned dictionaries. We evaluate the performance of the proposed method on six public datasets and compared against those of seven benchmark methods. The experimental results demonstrate the effectiveness and superiority of the proposed method in image classification over the benchmark dictionary learning methods.
Keywords:
Class-specific information
class-shared information
double graph regularization
double dictionary learning
image classification
similarity preserving
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
13.7
Papers:
1.0W
Citations:
8.4W