Image classification via convolutional sparse coding

The Convolutional Sparse Coding (CSC) model has recently attracted a lot of attention in the signal and image processing communities. Since, in traditional sparse coding methods, a significant assumption is that all input samples are independent, so it is not well for most dependent works. In such c...

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Vydáno v:The Visual computer Ročník 39; číslo 5; s. 1731 - 1744
Hlavní autoři: Nozaripour, Ali, Soltanizadeh, Hadi
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2023
Springer Nature B.V
Témata:
Algorithms
Artificial Intelligence
Classification
Coding
Computer Graphics
Computer Science
Image classification
Image processing
Image Processing and Computer Vision
Image reconstruction
Methods
Original Article
Training
Sparse representation
Filters
Feature map
Convolutional sparse coding
Image classification
ISSN:0178-2789, 1432-2315
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Shrnutí:The Convolutional Sparse Coding (CSC) model has recently attracted a lot of attention in the signal and image processing communities. Since, in traditional sparse coding methods, a significant assumption is that all input samples are independent, so it is not well for most dependent works. In such cases, CSC models are a good choice. In this paper, we proposed a novel CSC-based classification model which combines the local block coordinate descent (LoBCoD) algorithm with the classification strategy. For this, in the training phase, the convolutional dictionary atoms (filters) of each class are learned by all training samples of the same class. In the test phase, the label of the query sample can be determined based on the reconstruction error of the filters related to every subject. Experimental results on five benchmark databases at the different number of training samples clearly demonstrate the superiority of our method to many state-of-the-art classification methods. Besides, we have shown that our method is less dependent on the number of training samples and therefore it can better work than other methods in small databases with fewer samples. For instance, increases of 26.27%, 18.32%, 11.35%, 13.5%, and 19.3% in recognition rates are observed for our method when compared to conventional SRC for five used databases at the least number of training samples per class.
Bibliografie:ObjectType-Article-1
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ISSN:0178-2789
1432-2315
DOI:10.1007/s00371-022-02441-1