基于两层迁移卷积神经网络的抽象图像情感识别

Recognizing emotions from abstract paintings using convolutional neural network with two-layer transfer learning scheme

为弥合抽象图像底层视觉特征与高层情感语义间的鸿沟,同时缓解抽象图像情感识别所固有的小样本缺陷,将两层迁移学习策略引入传统的卷积神经网络,提出一种基于两层迁移卷积神经网络的抽象图像情感识别模型.该模型利用深度特征的层次性,首先通过大规模通用图像数据集来学习提取普适的底层图像特征;然后利用抽象图像风格分类数据集来学习提取抽象图像的专有高层语义特征;最后采用抽象图像情感识别数据集来微调整个网络.MART数据集上的实验结果表明,与传统的抽象图像情感识别方法相比,所提出的模型能够有效地提高识别精度.

In order to bridge the gap between low-level visual features and high-level emotional semantics,and to alleviate the defects inherent in small sample dataset in abstract paintings emotions recognition datasets,a two-layer transfer learning strategy is introduced into traditional convolutional neural networks and a model for recognizing emotions from abstract paintings is proposed using convolutional neural network with a two-layer transfer learning scheme. According to the hierarchical nature of deep features,a large-scale generalized image dataset is used to learn how extract universal low-level image features. Then the relevant domain dataset is utilized to learn how extract specific high-level semantic features. Finally the abstract painting emotion recognition dataset is used to finetune the network. As shown by our extensive experimental validation on MART datasets,the proposal outperforms current methods when recognizing emotions from abstract paintings.