Enterprise AI Analysis
Fusion of deep transfer learning models with Gannet optimisation algorithm for an advanced image captioning system for visual disabilities
Unlocking the potential of advanced image captioning for enhanced accessibility and operational efficiency.
Executive Impact: At a Glance
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Time Savings
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Accuracy Boost
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Cost Reduction
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Innovation Score
Deep Analysis & Enterprise Applications
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Relevance for Enterprise
This research addresses the critical need for advanced image captioning systems to improve accessibility for visually impaired individuals, leveraging deep transfer learning and optimization algorithms to generate highly accurate and context-aware descriptions. The FDTLGO-AICSVD model significantly enhances the quality of life for people with visual disabilities by enabling them to quickly understand visual content through automated spoken captions.
Executive Summary
This paper introduces FDTLGO-AICSVD, a novel image captioning system that uses a fusion of deep transfer learning models (DenseNet121, VGG19, MobileNetV2) and the Gannet Optimisation Algorithm (GOA) for hyperparameter tuning. Designed to assist visually impaired individuals, the system preprocesses images (noise removal, contrast enhancement) and text (standardization, numbers removal, lowercasing, vectorization) to generate precise and context-aware captions. Extensive experimentation on Flickr8k and Flickr30k datasets demonstrates superior performance, achieving BLEU-4 scores of 45.11% and 58.91%, and CIDEr scores of 63.17 and 69.81, respectively, outperforming existing models.
Key Strengths
- Robust multi-model fusion (DenseNet121, VGG19, MobileNetV2)
- Optimized with Gannet Algorithm for hyperparameter tuning
- Advanced image and text preprocessing for clarity
- Superior BLEU-4 and CIDEr scores on Flickr8k/Flickr30k
- Significantly reduced computational time
Key Performance Metric
45.11%
Superior BLEU-4 Score on Flickr8K
Enterprise Process Flow
Image Preprocessing (Noise Removal & Contrast Enhancement)
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Text Preprocessing (Standardize, Remove Numbers, Lowercase, Vectorize)
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Feature Extraction (DenseNet121, VGG19, MobileNetV2 & TF-IDF)
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Hyperparameter Tuning (GOA)
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Caption Generation
Performance Benchmarking: FDTLGO-AICSVD vs. Leading Models (Flickr8K)
| Model | BLEU-4 | CIDEr |
|---|---|---|
| FDTLGO-AICSVD | 45.11% | 63.17% |
| AIC-SSAIDL | 33.41% | 47.89% |
| LSAHCNN-ICS | 37.53% | 58.20% |
| CNN Method | 28.39% | 42.82% |
| SCA-CNN-VGG | 26.21% | 40.03% |
| Hard-Attention | 24.73% | 38.18% |
| Soft-Attention | 21.99% | 35.51% |
| NIC | 19.94% | 32.83% |
Computational Efficiency Highlight
5.12s
Reduced CT (Flickr8K)
Real-World Impact & Scalability
Real-World Impact on Flickr30K
The FDTLGO-AICSVD model achieved a BLEU-4 score of 58.91% and a CIDEr score of 69.81% on the Flickr30K dataset, demonstrating significantly enhanced descriptive accuracy and language generation capabilities, crucial for assisting visually impaired individuals in diverse scenarios.
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Estimated Annual Savings
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Hours Reclaimed Annually
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Your AI Implementation Roadmap
A structured approach to integrating FDTLGO-AICSVD into your enterprise for maximum impact.
Phase 1: Pilot & Integration (2-4 Weeks)
Initial assessment, data preparation, and integration of FDTLGO-AICSVD into a limited environment to demonstrate initial value and validate core functionalities. Focus on critical use cases for visually impaired accessibility.
Phase 2: Scalable Deployment (4-8 Weeks)
Expansion of the system to a broader user base, optimizing for performance and scalability. This includes fine-tuning the Gannet optimization algorithm for diverse image datasets and real-time captioning requirements.
Phase 3: Advanced Optimization & Monitoring (Ongoing)
Continuous monitoring, performance tuning, and iterative improvements based on user feedback. Integration of new deep learning models and further algorithm enhancements to maintain state-of-the-art accuracy and efficiency.
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