Image similarity search via hashes with expanded dimensionality and sparsification

A similarity search system comprising:

• one or more processors;
• and
• memory coupled to the one or more processors, wherein the memory comprises computer-executable instructions causing the one or more processors to perform a process comprising:
• for a query image, generating a query image hash via a hash model, wherein generating the query image hash comprises expanding dimensionality of a query image feature vector representing the query image and sparsifying the hash after expanding dimensionality;
• matching the query image hash against hashes in a sample image hash database, wherein the hashes in the sample image hash database are previously generated via the hash model for respective sample images and represent the respective sample images, and wherein the matching identifies one or more matching hashes in the database;
• and
• outputting the one or more matching hashes as a result of the similarity search.

One or more computer-readable media having encoded thereon computer-executable instructions that, when executed, cause a computing system to perform a similarity search method comprising:

• receiving one or more sample images;
• extracting feature vectors from the sample images, the extracting generating sample image feature vectors;
• normalizing the sample image feature vectors;
• with a hash model, generating sample image hashes from the normalized sample image feature vectors, wherein the hash model expands dimensionality of the normalized sample image feature vectors and subsequently sparsifies the sample image hashes after expanding dimensionality;
• storing the hashes generated from the normalized sample image feature vectors into a sample image hash database;
• receiving a query image;
• extracting a feature vector from the query image, the extracting generating a query image feature vector;
• normalizing the query image feature vector;
• with the hash model, generating a query image hash from the normalized query image feature vector, wherein the hash model expands dimensionality of the normalized query image feature vector and subsequently sparsifies the query image hash after expanding dimensionality;
• matching the query image hash against hashes in the sample image hash database;
• and
• outputting matching sample image hashes of the sample image hash database as a result of the similarity search.