Art design integrating visual relation and affective semantics based on Convolutional Block Attention Mechanism-generative adversarial network model

Scene-based image semantic extraction and its precise sentiment expression significantly enhance artistic design. To address the incongruity between image features and sentiment features caused by non-bilinear pooling, this study introduces a generative adversarial network (GAN) model that integrate...

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Bibliographic Details
Published in:PeerJ. Computer science Vol. 10; p. e2274
Main Authors: Shen, Jiadong, Wang, Jian
Format: Journal Article
Language:English
Published: United States PeerJ. Ltd 30.08.2024
PeerJ Inc
Subjects:
Adaptive and Self-Organizing Systems
Algorithms and Analysis of Algorithms
Art design
Artificial Intelligence
CBEAM
GAN
Liquors
LSTM
Neural Networks
Semantics
Social Computing
Visual communication
Art design
LSTM
Visual communication
GAN
CBEAM
ISSN:2376-5992, 2376-5992
Online Access:Get full text
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Summary:Scene-based image semantic extraction and its precise sentiment expression significantly enhance artistic design. To address the incongruity between image features and sentiment features caused by non-bilinear pooling, this study introduces a generative adversarial network (GAN) model that integrates visual relationships with sentiment semantics. The GAN-based regularizer is utilized during training to incorporate target information derived from the contextual information into the process. This regularization mechanism imposes stronger penalties for inaccuracies in subject-object type predictions and integrates a sentiment corpus to generate more human-like descriptive statements. The capsule network is employed to reconstruct sentences and predict probabilities in the discriminator. To preserve crucial focal points in feature extraction, the Convolutional Block Attention Mechanism (CBAM) is introduced. Furthermore, two bidirectional long short-term memory (LSTM) modules are used to model both target and relational contexts, thereby refining target labels and inter-target relationships. Experimental results highlight the model’s superiority over comparative models in terms of accuracy, BiLingual Evaluation Understudy (BLEU) score, and text preservation rate. The proposed model achieves an accuracy of 95.40% and the highest BLEU score of 16.79, effectively capturing both the label content and the emotional nuances within the image.
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ISSN:2376-5992
2376-5992
DOI:10.7717/peerj-cs.2274