To implement visual search (image to product) using vectors, you convert images into numerical representations called embeddings, store them in a vector database, and compare them to find similar products. This approach relies on deep learning models to extract meaningful features from images and vector similarity metrics to match them efficiently. The process involves three main steps: generating image embeddings, indexing them for fast retrieval, and querying the database with a new image to find matches.
First, images are transformed into vectors using a pre-trained neural network like ResNet or CLIP. These models process an image through layers of neurons, outputting a high-dimensional vector (e.g., 512 or 1024 numbers) that captures visual features such as shapes, textures, or patterns. For example, a shoe image might be encoded into a vector where similar shoes (e.g., sneakers with white soles) cluster near each other in the vector space. Tools like TensorFlow or PyTorch simplify this step by providing pre-trained models and APIs for inference. Before generating embeddings, images are typically resized, normalized, and preprocessed to match the model’s input requirements.
Next, the vectors are stored in a specialized database optimized for similarity searches, such as FAISS, Milvus, or Elasticsearch with vector plugins. These databases use indexing techniques like hierarchical navigable small worlds (HNSW) or inverted files (IVF) to organize vectors for fast retrieval. For instance, an e-commerce platform might index millions of product images, enabling queries to return results in milliseconds. Indexes balance speed and accuracy: approximate nearest neighbor (ANN) algorithms prioritize performance over exact matches, which is practical for large datasets. Metadata like product IDs or categories can be linked to vectors for filtering results post-search.
Finally, when a user submits a query image, the system generates its embedding and searches the database for the closest vectors. Similarity metrics like cosine similarity or Euclidean distance measure how “close” the query vector is to stored vectors. For example, a user uploading a photo of a chair could retrieve visually similar products ranked by similarity scores. To improve results, you might combine vector search with metadata filters (e.g., price range) or rerank top matches using a more precise (but slower) model. APIs like those in AWS Rekognition or Google Vision AI abstract parts of this pipeline, but custom implementations allow tighter control over performance and scalability.