A Social Network Image Classification Algorithm Based on Multimodal Deep Learning

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 lear...

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Vydané v:International journal of computers, communications & control Ročník 15; číslo 6
Hlavní autori: Bai, Junwei, Chi, Cheng
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oradea Agora University of Oradea 01.12.2020
Predmet:
Algorithms
Ant colony optimization
Clustering
Data structures
Deep learning
Feature extraction
Image classification
Machine learning
Neural networks
Similarity
Social networks
ISSN:1841-9836, 1841-9844
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1841-9836
1841-9844
DOI:10.15837/ijccc.2020.6.4037