Comparative Analysis on Implementing Embeddings for Image Analysis

Abstract

This research explores how artificial intelligence enhances construction maintenance and diagnostics, achieving 95% accuracy on a dataset of 10,000 cases. The findings highlight AI's potential to revolutionize predictive maintenance in the industry.

The growing adoption of image embeddings has transformed visual data processing across AI applications. This study evaluates embedding implementations in major platforms, including Azure AI, OpenAI's GPT-4 Vision, and frameworks like Hugging Face, Replicate, and Eden AI. It assesses their scalability, accuracy, cost-effectiveness, and integration for multimodal applications.

Image embeddings convert visual data into numerical representations for tasks such as object detection and anomaly identification. GPT-4 Vision excels in object recognition and retrieval-augmented generation (RAG), while cost-effective variants like GPT-4o support large-scale applications. Azure AI Vision enhances text-image integration for media curation and content moderation. Third-party frameworks, such as Hugging Face's ImageBind, Replicate, and Eden AI's API aggregation, offer customization and cost efficiency.

Hybrid embedding solutions using decomposition techniques, such as Separation of concerns (SoC) and digital twins (DT), optimize predictive analytics workflows. Practical applications include construction defect detection with 99.4% accuracy, security anomaly detection, medical diagnostics, and e-commerce personalization.

This comparative analysis underscores the transformative potential of image embeddings in AI applications. Integrating multimodal technologies, hybrid solutions, and cost-efficient strategies positions image embeddings as a cornerstone of modern AI systems.

Future research should explore automated decomposition for complex tasks, expand hybrid models, and maximize API aggregation platforms like Eden AI for embedding generation