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Image aesthetic assessment with weighted multi-region aggregation based on information theory

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Abstract

Image aesthetic assessment is a hot issue in current research. It will be very important to find regions that affect the aesthetic assessment of the image, for which we propose a weighted multi-region aesthetic assessment network WRMA-Net, which consists of three modules: information theory-based image segmentation module uses information theory to segment images; in the feature extraction module, we connect Convolutional Neural Network(CNN) and Graph Neural Network(GNN) in tandem, using CNN to obtain shallow detail information of the image and GNN to obtain deep semantic information of the image, which can retain feature information at each level, and subsequently fuse shallow and deep features to predict aesthetic assessment scores by the region weighting module; the weighted multi-region aggregation module assigns different weights to each region adaptively to adjust the prediction results and find high-quality aesthetic regions. The network can analyze image aesthetics from multiple regions and provide constructive regional aesthetic suggestions. The experimental results show that our WMRA-Net achieves good results in some aesthetic assessment metrics.

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Authors and Affiliations

  1. School of Computer Science and Technology, Tiangong University, Tianjin, 300387, China

    Yin Wang, Jing Guo, Yongzhen Ke, Kai Wang & Shuai Yang

  2. Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems, Tianjin, 300387, China

    Jing Guo, Yongzhen Ke, Kai Wang & Shuai Yang

  3. National Demonstration Center for Experimental Engineering Training Education, Tianjin, 300387, China

    Yongzhen Ke

  4. Fitow (Tianjin) Detection Technology Co., Ltd., Tianjin, 300071, China

    Liming Chen

Authors
  1. Yin Wang
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  2. Jing Guo
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  3. Yongzhen Ke
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  4. Kai Wang
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  5. Shuai Yang
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  6. Liming Chen
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Contributions

Yin Wang: Methodology, Software, Writing - Original Draft.Jing Guo: Conceptualization, Supervision, Validation, Writing - Review & EditingYongzhen Ke: Conceptualization, Supervision, Methodology, Writing - Review & Editing.Kai Wang: Writing - Review & Editing, Formal analysis, Visualization.Shuai Yang: Resources, Validation, Data Curation.Liming Chen: Methodology, Writing - Review & Editing.

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Correspondence to Yongzhen Ke.

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Wang, Y., Guo, J., Ke, Y. et al. Image aesthetic assessment with weighted multi-region aggregation based on information theory. Pattern Anal Applic 28, 115 (2025). https://doi.org/10.1007/s10044-025-01490-1

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