基于卷积神经网络和代价敏感的不平衡图像分类方法

Imbalanced image classification approach based on convolution neural network and cost-sensitivity

针对不平衡图像分类中少数类查全率低、分类结果总代价高,以及人工提取特征主观性强而且费时费力的问题,提出了一种基于Triplet-sampling的卷积神经网络(Triplet-sampling CNN)和代价敏感支持向量机(CSSVM)的不平衡图像分类方法——Triplet-CSSVM。该方法将分类过程分为特征学习和代价敏感分类两部分。首先,利用误差公式为三元损失函数的卷积神经网络端对端地学习将图像映射到欧几里得空间的编码方法;然后,结合采样方法重构数据集,使其分布平衡化;最后,使用CSSVM分类算法给不同类别赋以不同的代价因子,获得最佳代价最小的分类结果。在深度学习框架Caffe上使用人像数据集FaceScrub进行实验。实验结果表明,所提方法在1∶3的不平衡率下,与VGGNet-SVM方法相比,少数类的精确率提高了31个百分点,召回率提高了71个百分点。

Focusing on the issues that the recall of minority class is low, the cost of classification is high and manual feature selection costs too much in imbalanced image classification, an imbalanced image classification approach based on Triplet-sampling Convolutional Neural Network （Triplet-sampling CNN） and Cost-Sensitive Support Vector Machine （CSSVM）, called Triplet-CSSVM, was proposed. This method had two parts：feature learning and cost sensitive classification. Firstly, the coding method which mapped images to a Euclidean space end-to-end was learned by the CNN which used Triplet loss as loss function. Then, the dataset was rescaled by sampling method to balance the distribution. At last, the best classification result with the minimum cost was obtained by CSSVM classification algorithm which assigned different cost factors to different classes. Experiments with the portrait dataset FaceScrub on the deep learning framework Caffe were conducted. And the experimental results show that the precision is increased by 31 percentage points and the recall of the proposed method is increased by 71 percentage points compared with VGGNet-SVM （Visual Geometry Group Net-Support Vector Machine） in the condition of 1：3 imbalanced rate.