To create an image search engine, first build a feature extraction pipeline. Use pre-trained models like ResNet or VGG16 to extract feature vectors from images. Store these vectors in a searchable database like Elasticsearch or FAISS.
When a user inputs an image, preprocess it and extract its feature vector using the same pipeline. Search the database for similar vectors using similarity metrics like cosine similarity or Euclidean distance. Return the most similar images as results.
Enhance the engine with a user-friendly interface and scalability features like caching and distributed storage. For improved performance, fine-tune the feature extractor on domain-specific datasets.