How does similarity scoring work in image search?

Similarity scoring in image search works by comparing numerical representations of images to find visually or semantically similar matches. The process involves three main steps: feature extraction, vector comparison, and efficient search. Each image is converted into a feature vector—a list of numbers capturing its visual attributes—using deep learning models like CNNs (Convolutional Neural Networks). For example, a pretrained ResNet model might generate a 1,024-dimensional vector representing edges, textures, or object shapes in an image. These vectors act as unique fingerprints, where similar images produce vectors that are mathematically closer.

The actual scoring relies on distance metrics to measure how “far apart” two vectors are. Common methods include cosine similarity (measuring the angle between vectors) and Euclidean distance (straight-line distance in vector space). Cosine similarity is often preferred because it focuses on directional alignment, making it robust to differences in image brightness or scale. For instance, a query image of a red car might have a high cosine similarity with another red car image, even if one is darker or slightly rotated. These metrics are computed between the query image’s vector and all vectors in the database, ranking results by their proximity.

Practical implementations optimize for speed and scalability. Comparing every query against millions of vectors in real time is computationally expensive, so tools like FAISS or Annoy (Approximate Nearest Neighbors Oh Yeah) are used to index vectors for fast approximate searches. These libraries trade a small accuracy loss for significant speed gains. For example, FAISS might use clustering to group similar vectors, reducing the search space. The choice of feature extraction model also impacts results: a model trained on object recognition (e.g., MobileNet) will prioritize shapes, while one fine-tuned on textures might better match patterns. Developers can adjust these components based on their specific use case, balancing accuracy, latency, and resource constraints.