A Social Network Image Classification Algorithm Based on Multimodal Deep Learning

  • Junwei Bai
  • Cheng Chi Hubei University of Technology


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.


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How to Cite
BAI, Junwei; CHI, Cheng. A Social Network Image Classification Algorithm Based on Multimodal Deep Learning. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 15, n. 6, nov. 2020. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/4037>. Date accessed: 30 nov. 2020. doi: https://doi.org/10.15837/ijccc.2020.6.4037.