How would you build the Google Images Search Engine?

Is Google Photos becoming too restricted for you?

Google Image is a search engine where we input a text or an image query and are presented with a ranked list of related images. If we use text as input, we want to make sure that the images are well described by the text, and if we use an image as input, we want to make sure to present the most similar images.

We can frame this problem as a ranking problem. We need a model that takes as input two images and returns a similarity score. Using that model, we can then rank the images based on that similarity score. A typical modeling approach is to utilize models that can learn a vectorial representation (embedding) of the images and compute a similarity metric on those vectors. We need a model that can extract the image features to learn a vector representation of images, and we need a model that can extract the text features to learn a vector representation of text inputs. We need to co-train the image and text models so the vector representations are semantically aligned.

To ensure fast retrieval, we need a way to store the existing images and quickly search for similar images. Since we are encoding the images into their vector representations, it seems logical to index the images into a vector database. The indexing pipeline converts the original images into their vector representations and indexes them into a vector database.

When a user inputs a text or image query, we need to return a list of images. The embedding generation service generates an embedding encoding of the input query. The embedding query is sent to the vector database that returns the nearest neighbors of the query. The reranking service is mainly used to rerank the nearest neighbors using a better model than the embedding generation model. It could be used to personalize the ranking to the specific user by using user-specific data. The resulting list is a list of image IDs, and it is then sent to the image store to retrieve the actual images to return to the user.

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