图像分类中的特征提取分析：综合研究
1. 旁遮普大学, 植物学研究所, 巴基斯坦拉合尔 51000
2. 深圳大学, 计算机科学与软件工程学院, 广东深圳 518060
| 摘 要： | 已经有许多利用深度学习的实际应用，特别是在图像分类领域。一个常见的发现是，某些领域数据高度倾斜，这意味着大部分信息属于少数多数类别，而少数类别中的信息很少或没有。重要的是要承认，倾斜的类别分布对机器学习算法构成了重大挑战。因此，在数据分布不平衡的情况下，大多数机器和深度学习算法在高度不平衡时无效或可能失败。在本研究中，考虑基于深度学习的知名模型，对不平衡数据集进行全面分析。特别是，确定了最佳特征提取器模型，并检查了最新特征提取模型的当前趋势。此外，还进行了1991年至2022年的文献计量分析，以确定全球关于不平衡蘑菇数据集图像分类的科学研究。总之，我们的研究结果可以为研究人员提供快速基准测试参考和替代方法来评估图像分类研究中不平衡数据分布的趋势。 |
| 关 键 词： | 蘑菇分类; 文献计量分析; 特征提取; 分类准确率 |
| DOI： | 10.57237/j.cst.2023.04.001 |
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收稿日期: 2023-08-12
接受日期: 2023-10-07
在线出版日期: 2023-10-09
An Analysis of Feature Extraction in Image Classification: A Comprehensive Study
1. Institute of Botany, University of the Punjab, Lahore 51000, Pakistan
2. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
| Abstract: | There have been many real-life applications utilizing deep learning, especially in the area of image classification. A common finding is that some domain data are highly skewed, which means that most of the information belongs to a small number of majority classes, and there is little or no information in the minority classes. Due to which in case of imbalanced data distribution, the majority of machine and deep learning algorithms are not effective or may fail when it is highly imbalanced. In this study, a comprehensive analysis of imbalanced dataset is conducted by considering deep learning-based well-known models. In particular, the best feature extractor model is identified and the current trend of latest feature extraction model is examined. Moreover, a bibliometric analysis is carried out from 1991 to 2022 in order to identify the global scientific research on the image classification of imbalanced mushroom dataset. In summary, our findings may offer researchers a quick benchmarking reference and alternative approach to assessing trends in imbalanced data distributions in image classification research. |
| Keywords: | Mushroom Classification; Bibliometric Analysis; Features Extraction; Classification Accuracy |
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