Nowadays, deep learning technique becomes the most popular fast-growing machine learning method in an Artificial Neural Network. The Convolution Neural Network (CNN) is one of the deep learning architecture that has been applied in the field of image analysis and image classification. In this paper, we proposed a novel emotion learning model with a deep learning network. The aim of the learning model is to reduce the affective gap, that extracts the objects and background features of an image semantically, such as high-level and low-level features. These extracted features accompanied with few others and it is more effective in emotion prediction model based on visual concepts of image, that leads to better emotion recognition performance. For training and testing, the experiment is conducted on IAPS (International Affective Picture System) dataset, the Artistic Photos, and the Emotion-Image dataset. An experimental result shows that the proposed model combines visual-content and low-level features of the image that provides promising results for Affective Emotion Classification task.

如今，深度学习技术已成为人工神经网络中最流行的快速增长的机器学习方法。卷积神经网络（CNN）是已在图像分析和图像分类领域中应用的深度学习架构之一。在本文中，我们提出了一种具有深度学习网络的新型情感学习模型。学习模型的目的是减少情感鸿沟，从语义上提取图像的对象和背景特征（例如高级特征和低级特征）。这些提取的特征很少伴随其他特征，并且在基于图像的视觉概念的情感预测模型中更有效，从而导致更好的情感识别性能。为了进行培训和测试，实验是在IAPS（国际情感图片系统）数据集，Artistics Photos和Emotion-Image数据集上进行的。实验结果表明，所提出的模型结合了视觉内容和图像的低级特征，为情感情感分类任务提供了有希望的结果。