基于多层次深度卷积神经网络的图像情感分类

A Multi-level Deep Convolutional Neural Network for Image Emotion Classification

由于图像的复杂性和人类情感的主观性,图像情感分类是一项非常具有挑战性的任务.针对深度学习方法没有充分考虑图像先验信息的问题,提出一个新的多层次深度卷积神经网络框架.该框架综合考虑全局和局部视角,引入显著主体、颜色和局部等先验信息,从多个层次学习图像的情感表达.实验结果表明,在公开的大数量级和小数量级情感图库上,该框架的分类准确率均高于现有的图像情感分类方法,其平均分类准确率比最优方法提高了2.8%,特别在情感类别“厌恶”上提高了15%.

Emotion classification of images is a challenging task regarding the complexity of various images and the subjectivity of human s emotion perception. Most existing deep learning methods didn t consider image prior information fully. A new multi-level deep convolutional neural network was proposed to predict the emotion based on the multi-level prior information from global and local view. Extensive experiments on both the large scale and small scale emotion datasets demonstrate the effectiveness of our method. The average classification accuracy of our method is 2.8% higher than the state-of-art method, especially 15% higher in the category “disgust”.