.png){kind=icon}
.png){kind=icon}
Return
Low-rank double dictionary learning from corrupted data for robust image classification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2017.06.038.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, we propose a novel low-rank double dictionary learning ((LRDL)-L-2) method for robust image classification tasks, in which the training and testing samples are both corrupted. Unlike traditional dictionary learning methods, (LRDL)-L-2 simultaneously learns three components from corrupted training data: 1) a low-rank class-specific sub-dictionary for each class to capture the most discriminative class-specific features of each class, 2) a low-rank class-shared dictionary which models the common patterns shared in the data of different classes, and 3) a sparse error term to model the noise in data. Through low-rank class-shared dictionary and noise term, the proposed method can effectively separate the corruptions and noise in training samples from creating low-rank class-specific sub-dictionaries, which are employed for correctly reconstructing and classifying testing images. Comparative experiments are conducted on three public available databases. Experimental results are encouraging, demonstrating the effectiveness of the proposed method and its superiority in performance over the state-of-the-art dictionary learning methods. (C) 2017 Elsevier Ltd. All rights reserved.
Keywords:
Low-rank dictionary learning
Class-specific dictionary
Class-shared dictionary
Image classification
Corrupted training samples
Robustness
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.6
Papers:
1.3W
Citations:
4.5W