基于跨领域卷积稀疏自动编码器的抽象图像情绪性分类

Affective Abstract Image Classification Based on Convolutional Sparse Autoencoders across Different Domains

为了将无监督特征学习应用于小样本量的图像情绪语义分析,该文采用一种基于卷积稀疏自动编码器进行自学习的领域适应方法对少量有标记抽象图像进行情绪性分类。并且提出了一种采用平均梯度准则对自动编码器所学权重进行排序的方法,用于对基于不同领域的特征学习结果进行直观比较。首先在源领域中的大量无标记图像上随机采集图像子块并利用稀疏自动编码器学习局部特征,然后将对应不同特征的权重矩阵按照每个矩阵在3个色彩通道上的平均梯度中的最小值进行排序。最后采用包含池化层的卷积神经网络提取目标领域有标记图像样本的全局特征响应,并送入逻辑回归模型进行情绪性分类。实验结果表明基于自学习的领域适应可以为无监督特征学习在有限样本目标领域上的应用提供训练数据,而且采用稀疏自动编码器的跨领域特征学习能在有限数量抽象图像情绪语义分析中获得比底层视觉特征更优秀的辨识效果。

To apply unsupervised feature learning to emotional semantic analysis for images in small sample size situations, convolutional sparse autoencoder based self-taught learning for domain adaption is adopted for affective classification of a small amount of labeled abstract images. To visually compare the results of feature learning on different domains, an average gradient criterion based method is further proposed for the sorting of weights learned by sparse autoencoders. Image patches are first randomly collected from a large number of unlabeled images in the source domain and local features are learned using a sparse autoencoder. Then the weight matrices corresponding to different features are sorted according to the minimal average gradient of each matrix in three color channels. Global feature activations of labeled images in the target domain are finally obtained by a convolutional neural network including a pooling layer and sent into a logistic regression model for affective classification. Experimental results show that self-taught learning based domain adaption can provide training data for the application of unsupervised feature learning in target domains with limited samples. Sparse autoencoder based feature learning across different domains can produce better identification effect than low-level visual features in emotional semantic analysis of a limited number of abstract images.