Volume 37, Number 3, June 2019
|Page(s)||465 - 470|
|Published online||20 September 2019|
Classification of Few Labeled Images Based on Integrated GMM Clustering
School of Astronautics, Northwestern Polytechnical University, Xi’an, 710072, China
2 Air Defense Academy, Air Force Engineering University, Xi'an 710043, China
In order to improve the classifier classification accuracy of by using convolutional neural network training, a large amount of labeled data is often required, but sometimes labeled data is not easily obtained.This paper proposes a solution based on the idea of integrated GMM clustering and label delivery for classifying images with few labeled samples, assigning tags to unlabeled data through certain rules, and converting unlabeled data into labeled data for training of the model.In this paper, experiments are performed on hand-written digital recognition data sets. The results show that the present algorithm has a great improvement in the accuracy of model classification comparing with the method of using only labeled samples in the case of few labeled samples. The effectiveness of the present algorithm is validated.
Key words: integrated GMM clustering / few labeled samples / voting rules
关键字 : 集成GMM聚类 / 少标记样本 / 投票规则
© 2019 Journal of Northwestern Polytechnical University. All rights reserved.
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