Journal
ELECTRONICS
Volume 11, Issue 6, Pages -Publisher
MDPI
DOI: 10.3390/electronics11060847
Keywords
multiple vector representation; sparse representation; dictionary learning; image classification
Categories
Funding
- Research Foundation for Advanced Talents of Guizhou University [49]
- Key Disciplines of Guizhou Province Computer Science and Technology [ZDXK[2018]007]
- Research Projects of Innovation Group of Education [[2021]022]
- Guizhou Province Graduate Research Fund [YJSCXJH[2020]53, YJSCXJH[2020]189]
- National Natural Science Foundation of China [62062023]
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In this paper, a robust dictionary learning method based on image multi-vector representation is proposed to balance large-scale information and global features. The method generates a virtual image and obtains multi-vector representation for better image classification accuracy.
Each sparse representation classifier has different classification accuracy for different samples. It is difficult to achieve good performance with a single feature classification model. In order to balance the large-scale information and global features of images, a robust dictionary learning method based on image multi-vector representation is proposed in this paper. First, this proposed method generates a reasonable virtual image for the original image and obtains the multi-vector representation of all images. Second, the same dictionary learning algorithm is used for each vector representation to obtain multiple sets of image features. The proposed multi-vector representation can provide a good global understanding of the whole image contour and increase the content of dictionary learning. Last, the weighted fusion algorithm is used to classify the test samples. The introduction of influencing factors and the automatic adjustment of the weights of each classifier in the final decision results have a significant indigenous effect on better extracting image features. The study conducted experiments on the proposed algorithm on a number of widely used image databases. A large number of experimental results show that it effectively improves the accuracy of image classification. At the same time, to fully dig and exploit possible representation diversity might be a better way to lead to potential various appearances and high classification accuracy concerning the image.
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