Multimodal Image Search with GPT-4 Vision

Combining visual and textual analysis for improved image retrieval

Built an image search system that transcends traditional visual similarity by incorporating AI-driven textual understanding of image content and context.

Motivation

Traditional reverse image search relies purely on visual features, missing semantic relationships and contextual understanding. This limits retrieval quality when searching by concept rather than visual similarity.

Implementation

Hybrid search pipeline:

  1. GPT-4 Vision generates detailed textual descriptions of query images
  2. CLIP embeddings capture both visual and semantic features
  3. Dual-stream retrieval combines visual similarity with textual understanding

Applications

Applicable to e-commerce product discovery, enabling customers to search by showing product images or describing features in natural language, bridging the gap between visual and text-based search.

Code: GitHub Repository
Write-up: Medium Article