.png){kind=icon}
返回
Imbalanced object classification using a new convolutional neural network and principal component analysis
.png){kind=icon}
PRE
AI
DOI：10.1007/s11042-026-21321-7.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Deep learning techniques excel in balanced image classification but often struggle with imbalanced datasets, leading to poor recognition of minority-class samples. To address this, we propose a novel Convolutional Neural Network (CNN) architecture designed for imbalanced object classification. Our approach integrates an adaptive feature extraction mechanism that selectively fuses discriminative features when classification accuracy falls below a predefined threshold. Principal Component Analysis (PCA) reduces the dimensionality of feature vectors, minimizing computational complexity and memory requirements. Experimental results demonstrate that our method achieves 98.91% accuracy on the Caltech-101 dataset and 98.00% on the Pascal VOC 2012 dataset, outperforming several state-of-the-art techniques. These results validate the effectiveness of our approach in addressing class imbalance challenges.
Keyword:
Object classification
Deep learning
Convolutional neural network
Principal component analysis (PCA)
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
机构
引用论文
Image Classification Based on Light Convolutional Neural Network Using Pulse Couple Neural Network基于光卷积神经网络和脉冲耦合神经网络的图像分类
OAAI
OAAI分享收藏
Gradient-based learning applied to document recognition基于梯度的学习在文档识别中的应用.png)
PROCEEDINGS OF THE IEEE
PROCEEDINGS OF THE IEEEIF25.9
PREAI
分享收藏
查看更多内容