A Social Network Image Classification Algorithm Based on Multimodal Deep Learning

IAENG Internaitonal journal of computer science

article

Junwei Bai¹  Cheng Chi² 

[1] School of Art and Media, Wuhan Polytechnic University;Hubei University of Technology

关键词: multimodal deep learning;    social network;    image classification;    three-dimensional neural network (3D NN).;   

DOI  :  10.15837/ijccc.2020.6.4037

学科分类：计算机科学（综合）

来源: International Association of Engineers

【 摘 要 】

The complex data structure and massive image data of social networks pose a huge challenge to the mining of associations between social information. For accurate classification of social network images, this paper proposes a social network image classification algorithm based on multimodal deep learning. Firstly, a social network association clustering model (SNACM) was established, and used to calculate trust and similarity, which represent the degree of similarity between users. Based on artificial ant colony algorithm, the SNACM was subject to weighted stacking, and the social network image association network was constructed. After that, the social network images of three modes, i.e. RGB (red-green-blue) image, grayscale image, and depth image, were fused. Finally, a three-dimensional neural network (3D NN) was constructed to extract the features of the multimodal social network image. The proposed algorithm was proved valid and accurate through experiments. The research results provide a reference for applying multimodal deep learning to classify the images in other fields.

【 授权许可】

CC BY