Embedding similarity in image search is calculated by comparing numerical representations (embeddings) of images to determine how closely they match. When an image is processed through a neural network (like a CNN or transformer), the model generates a high-dimensional vector (embedding) that captures the image’s visual features. These embeddings are typically extracted from the network’s final layers before classification, where the model encodes semantic information like objects, textures, or patterns. For example, a photo of a dog might produce an embedding that emphasizes fur texture, ear shape, or background context. The similarity between two images is then measured by comparing their corresponding embedding vectors using mathematical metrics.
The most common method for comparing embeddings is calculating the distance between vectors. Cosine similarity is widely used because it measures the angle between vectors, making it robust to differences in vector magnitude (e.g., lighting variations in images). Euclidean distance (L2) is another option, which directly measures the straight-line distance between vectors. For instance, if two embeddings for cat images are close in Euclidean space, they likely share visual similarities. Manhattan distance (L1) is less common but can be useful for sparse embeddings. Developers often normalize embeddings to unit length before applying cosine similarity, as this simplifies the calculation to a dot product. Tools like PyTorch or TensorFlow provide built-in functions for these operations, allowing straightforward implementation.
To scale image search efficiently, embeddings are indexed using approximate nearest neighbor (ANN) algorithms like FAISS, Annoy, or HNSW. These tools organize embeddings into searchable structures (e.g., trees or graphs) to avoid comparing every pair of vectors exhaustively. For example, FAISS uses quantization to group similar vectors, reducing search time from hours to milliseconds for large datasets. In practice, a search system might process a query image into an embedding, then use ANN to retrieve the top-k most similar embeddings from the index. Real-world applications include reverse image search (Google Images) or product recommendations (e-commerce), where speed and accuracy depend on both the quality of embeddings and the choice of similarity metric and indexing strategy.
