Image Object Recognition via Deep Feature-Based Adaptive Joint Sparse Representation
Joint Authors
Wei, Wang
Can, Tang
Xin, Wang
Yanhong, Luo
Yongle, Hu
Ji, Li
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-11-21
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
An image object recognition approach based on deep features and adaptive weighted joint sparse representation (D-AJSR) is proposed in this paper.
D-AJSR is a data-lightweight classification framework, which can classify and recognize objects well with few training samples.
In D-AJSR, the convolutional neural network (CNN) is used to extract the deep features of the training samples and test samples.
Then, we use the adaptive weighted joint sparse representation to identify the objects, in which the eigenvectors are reconstructed by calculating the contribution weights of each eigenvector.
Aiming at the high-dimensional problem of deep features, we use the principal component analysis (PCA) method to reduce the dimensions.
Lastly, combined with the joint sparse model, the public features and private features of images are extracted from the training sample feature set so as to construct the joint feature dictionary.
Based on the joint feature dictionary, sparse representation-based classifier (SRC) is used to recognize the objects.
Experiments on face images and remote sensing images show that D-AJSR is superior to the traditional SRC method and some other advanced methods.
American Psychological Association (APA)
Wei, Wang& Can, Tang& Xin, Wang& Yanhong, Luo& Yongle, Hu& Ji, Li. 2019. Image Object Recognition via Deep Feature-Based Adaptive Joint Sparse Representation. Computational Intelligence and Neuroscience،Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1129608
Modern Language Association (MLA)
Wei, Wang…[et al.]. Image Object Recognition via Deep Feature-Based Adaptive Joint Sparse Representation. Computational Intelligence and Neuroscience No. 2019 (2019), pp.1-9.
https://search.emarefa.net/detail/BIM-1129608
American Medical Association (AMA)
Wei, Wang& Can, Tang& Xin, Wang& Yanhong, Luo& Yongle, Hu& Ji, Li. Image Object Recognition via Deep Feature-Based Adaptive Joint Sparse Representation. Computational Intelligence and Neuroscience. 2019. Vol. 2019, no. 2019, pp.1-9.
https://search.emarefa.net/detail/BIM-1129608
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1129608