System and method for determining item labels based on item images

Abstract

A method including automatically determining, by a machine learning model trained based at least in part on sample items stored in a sample database, a query embedding vector for a query image of a query item. The method further can include determining, based on a respective embedding distance between the query image of the query item and a respective image of each of the sample items, neighboring items from among the sample items. The respective embedding distance can be calculated based on the query embedding vector for the query image and a respective embedding vector for the respective image of each of the sample items. Each of the sample items can include the respective image and at least one respective item label. The method also can include determining a respective normalized weight for each of the neighboring items based on the respective embedding distance between the query image and the respective image of the each of the neighboring items. The method additionally can include determining a query item label of the query item based on a weighted majority vote by the neighboring items via the respective normalized weight for the each of the neighboring items. The method further can include upon determining that the query item label of the query item is different from a first item label of the query item. storing the query item with the query item label in a product database. The method also can include selectively updating the sample items stored in the sample database from items in the product database. In addition, the method can include re-training the machine learning model based at least in part on the sample items in the sample database, as updated. Other embodiments are disclosed.