What are the main algorithms used in image search?

The main algorithms used in image search focus on three core tasks: feature extraction, indexing, and similarity measurement. Feature extraction converts images into numerical representations, indexing organizes these representations for efficient retrieval, and similarity measurement compares these representations to find matches. Each step relies on specific algorithms optimized for scalability and accuracy in large datasets.

For feature extraction, traditional methods like SIFT (Scale-Invariant Feature Transform) and SURF (Speeded-Up Robust Features) detect and describe local image features, such as edges or corners, which are invariant to scale and rotation. These algorithms work well for tasks requiring geometric consistency but struggle with complex textures or semantic content. Modern approaches use convolutional neural networks (CNNs), such as ResNet or VGG, to generate high-dimensional feature vectors (embeddings) that capture semantic information. For example, a pre-trained ResNet model can process an image into a 2048-dimensional vector, encoding objects, colors, and patterns. Deep learning-based methods dominate due to their ability to generalize across diverse image types.

Indexing algorithms organize extracted features to enable fast retrieval. Locality-Sensitive Hashing (LSH) maps similar feature vectors to the same “hash buckets,” reducing search complexity. Tree-based structures like KD-trees or ANN (Approximate Nearest Neighbor) libraries (e.g., FAISS, Annoy) partition data into hierarchical clusters, allowing logarithmic-time lookups. For example, FAISS uses GPU acceleration and quantization to compress vectors, enabling billion-scale searches in milliseconds. These methods trade slight accuracy losses for significant speed improvements, making them practical for real-world applications.

Similarity measurement relies on distance metrics like Euclidean distance or cosine similarity to compare feature vectors. For binary hashes (e.g., LSH outputs), Hamming distance counts differing bits. Advanced systems combine these with filtering steps—such as re-ranking top candidates using more precise metrics—to balance speed and accuracy. For instance, a search pipeline might use FAISS to retrieve 100 approximate matches and then apply cosine similarity to reorder the top 10 results. This hybrid approach ensures both efficiency and relevance, adapting to the needs of applications like e-commerce product search or reverse image lookup.