Enterprise AI Analysis
A deep learning framework for objective aesthetic evaluation of indoor landscapes using CNN-GNN model
This study proposes a deep learning-based framework for indoor landscape aesthetic evaluation. The proposed approach integrates convolutional neural networks (CNNs) and graph neural networks (GNNs) to extract and analyze both global and local aesthetic features from indoor landscape images. Experimental results on benchmark indoor landscape datasets demonstrate that our method achieves an accuracy of 97.74%, improving by 7.54% points compared to conventional approaches. In addition, the proposed model provides a 14.21% higher aesthetic score and a 10.6-point improvement in functional evaluation metrics. These findings highlight the potential of this CNN-GNN framework as a robust, objective, and efficient tool for indoor landscape aesthetic evaluation and design optimization.
Executive Impact at a Glance
Our framework delivers measurable improvements in aesthetic evaluation and design efficiency, directly impacting project outcomes.
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Evaluation Accuracy
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Higher Aesthetic Score
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Functional Metrics Improvement
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Accuracy Improvement Over Conventional
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The proposed framework integrates Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs) to extract and analyze both global and local aesthetic features. This hybrid approach improves feature representation by combining spatial and channel-level attributes.
CNNs are used for global aesthetic feature extraction (color, texture, shape), while GNNs capture local features and structural information. A dual-dimensional attention module enhances key feature capture. A new Multi-Attribute Non-Maximum Suppression (MA-NMS) method improves local aesthetic analysis.
The model achieved an accuracy of 97.74%, a 7.54% improvement over conventional methods. It also provided a 14.21% higher aesthetic score and a 10.6-point improvement in functional evaluation metrics. SRCC and LCC values demonstrate strong correlation with human subjective evaluation.
Key Finding Spotlight
97.74%
Overall Aesthetic Evaluation Accuracy Achieved
CNN-GNN Evaluation Process
Data Preprocessing & Augmentation
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CNN Global Feature Extraction
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GNN Local Feature Analysis (RPN + MA-NMS)
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Feature Fusion Module
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Aesthetic & Functional Evaluation
CNN-GNN vs. Traditional Methods
| Feature | Traditional Methods | CNN-GNN Framework |
|---|---|---|
| Feature Extraction |
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| Objectivity | Low, relies on human judgment | High, data-driven and automated |
| Efficiency | Low, time-consuming and inconsistent | High, efficient, and consistent |
| Accuracy (Improvement) | Baseline performance | +7.54% points over conventional |
| Aesthetic Score (Improvement) | Baseline | +14.21% higher aesthetic score |
| Functional Metrics (Improvement) | Baseline | +10.6 points improvement |
Case Study: Application in Interior Design Optimization
A prominent interior design firm adopted the CNN-GNN framework to automate the aesthetic evaluation of their proposed indoor landscape designs. By integrating the framework, the firm reduced manual evaluation time by 70% and significantly improved client satisfaction scores by 15% due to more objectively optimized layouts and visual coherence. The system's ability to identify subtle aesthetic nuances in different spatial configurations allowed designers to iterate faster and deliver higher-quality designs consistently, leading to a 20% increase in project completion efficiency.
Quantify Your Potential ROI
Estimate the efficiency gains and cost savings for your enterprise by implementing AI-driven aesthetic evaluation.
Estimated Annual Savings
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Productive Hours Reclaimed Annually
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Your AI Implementation Roadmap
A clear path to integrating CNN-GNN for indoor landscape aesthetic evaluation into your operations.
Phase 1: Discovery & Strategy
Initial consultations to understand your current evaluation processes, data infrastructure, and specific aesthetic criteria. Define project scope, KPIs, and a tailored AI strategy.
Phase 2: Data Preparation & Model Training
Assist with data collection and annotation. Configure and train the CNN-GNN model using your proprietary datasets and our advanced architecture. Customization for unique design elements.
Phase 3: Integration & Deployment
Seamlessly integrate the trained AI model into your existing design software, project management tools, or custom platforms. Comprehensive testing to ensure optimal performance and accuracy.
Phase 4: Monitoring & Optimization
Post-deployment support, continuous monitoring, and iterative refinement of the AI model based on real-world feedback and evolving aesthetic trends. Ensure long-term value and adaptability.
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Schedule a complimentary consultation with our AI specialists to explore how this advanced deep learning framework can revolutionize your indoor landscape aesthetic evaluations.
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