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Diffusion is a type of passive transport. In passive transport, a substance tends to move from an area of high concentration to an area of low concentration until the concentration is equal across the space. For example, take the diffusion of substances through the air. When someone opens a perfume bottle in a room filled with people, the perfume is at its highest concentration in the bottle and is at its lowest at the edges of the room. The perfume vapor will diffuse, or spread away, from the...
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Unsupervised Content Mining in CBIR: Harnessing Latent Diffusion for Complex Text-Based Query Interpretation.

Venkata Rama Muni Kumar Gopu1, Madhavi Dunna1

  • 1Department of EECE, GITAM School of Technology, GITAM Deemed to be University, Rushikonda, Visakhapatnam 530045, India.

Journal of Imaging
|June 26, 2024
PubMed
Summary

This study introduces a new method for Content-Based Image Retrieval (CBIR) using latent diffusion models to interpret complex text prompts. This approach effectively connects text descriptions to visual content for advanced image searching.

Keywords:
CBIRcomplex queriescontent miningimage generationlatent diffusiontriplet networks

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Area of Science:

  • Computer Science
  • Artificial Intelligence

Background:

  • Traditional Content-Based Image Retrieval (CBIR) often relies on domain-specific queries.
  • Interpreting complex textual prompts for image retrieval presents a significant challenge.

Purpose of the Study:

  • To develop a novel methodology for Content-Based Image Retrieval (CBIR) that utilizes complex text-based queries.
  • To leverage latent diffusion models for interpreting intricate textual prompts and generating visual representations.

Main Methods:

  • Employed latent diffusion models to interpret complex textual prompts.
  • Designed a custom triplet network for feature representation and similarity assessment.
  • Utilized cosine similarity to retrieve relevant images based on feature representations.

Main Results:

  • Latent diffusion models successfully transformed complex textual queries into visual content.
  • The proposed method effectively bridged the gap between text prompts and image retrieval without labels or metadata.
  • Demonstrated the capability to retrieve images based on nuanced textual descriptions.

Conclusions:

  • The novel CBIR methodology offers a powerful approach for complex text-based image retrieval.
  • Latent diffusion models and triplet networks provide a robust framework for connecting textual meaning to visual data.
  • This advancement paves the way for future research in generative AI-driven image retrieval for big data applications.