# Unsupervised Content Mining in CBIR: Harnessing Latent Diffusion for Complex Text-Based Query Interpretation

## TL;DR

This paper introduces a new method for image retrieval using text prompts and AI to interpret complex queries without needing image labels.

## Contribution

The novel use of latent diffusion models and triplet networks for unsupervised text-based image retrieval is presented.

## Key findings

- Latent diffusion models effectively convert complex text into visual representations for image retrieval.
- Triplet networks trained with cosine similarity enable accurate retrieval without image labels.
- The method successfully bridges textual prompts and visual content in an unsupervised manner.

## Abstract

The paper demonstrates a novel methodology for Content-Based Image Retrieval (CBIR), which shifts the focus from conventional domain-specific image queries to more complex text-based query processing. Latent diffusion models are employed to interpret complex textual prompts and address the requirements of effectively interpreting the complex textual query. Latent Diffusion models successfully transform complex textual queries into visually engaging representations, establishing a seamless connection between textual descriptions and visual content. Custom triplet network design is at the heart of our retrieval method. When trained well, a triplet network will represent the generated query image and the different images in the database. The cosine similarity metric is used to assess the similarity between the feature representations in order to find and retrieve the relevant images. Our experiments results show that latent diffusion models can successfully bridge the gap between complex textual prompts for image retrieval without relying on labels or metadata that are attached to database images. This advancement sets the stage for future explorations in image retrieval, leveraging the generative AI capabilities to cater to the ever-evolving demands of big data and complex query interpretations.