Embedding similarity in image search is calculated by using vector representations of images, often referred to as embeddings. When an image is processed through a neural network, particularly a convolutional neural network (CNN), it generates a numerical representation that captures the essential features of the image. These embeddings are typically high-dimensional vectors. To find images similar to a query image, the system compares the embeddings using a similarity measure. Common methods for this comparison include Euclidean distance, cosine similarity, or more complex metrics.
To illustrate, consider a case where a developer has a collection of images stored in a database. When a user uploads a query image, the system generates its embedding using a pre-trained model. For example, the query image embedding might be a vector like [0.5, 0.2, 0.1, ...]. Each image in the database also has its own generated embedding. The developer would then calculate the similarity between the query image embedding and each of the database image embeddings to find the closest matches. If using cosine similarity, the system essentially measures the angle between two vectors, thereby indicating how similar their contents are, regardless of their size.
After calculating the similarity scores, the system ranks the images based on these scores and presents the most relevant results to the user. For effective performance, developers usually implement optimizations like indexing techniques (e.g., FAISS or Annoy) that allow for faster retrieval of similar embeddings in large datasets. This approach not only ensures that the search results are relevant but also efficient, enabling smooth user experiences in image search applications.