Systems and methods for product identification using image analysis from image mask and trained neural network

A computer system comprising:

• a data storage device storing processor-readable instructions;
• and
• a processor configured to execute the instructions to perform a method including:
• obtaining a first set of images, wherein each of the first set of images depicts a product;
• obtaining a first set of labels associated with the first set of images, wherein each of the first set of labels corresponds to an image in the first set of images and includes information indicating a mask of the product depicted in the corresponding image;
• training an image segmentation neural network based on the first set of images and the first set of labels;
• obtaining a second set of images, wherein each of the second set of images depicts a known product;
• obtaining a second set of labels associated with the second set of images, wherein each of the second set of labels corresponds to an image in the second set of images and includes information indicating classification information for the known product depicted in the corresponding image;
• training an image classification neural network based on the second set of predetermined images and the second set of labels;
• receiving a query image depicting a product that is not yet identified;
• performing image segmentation of the query image using the image segmentation neural network, wherein performing image segmentation of the query image includes removing background image portions of the query image, thereby obtaining a mask image of the product depicted in the query image;
• and
• identifying the product in the image by performing image analysis of the mask image of the product depicted in the query image using the image classification neural network.

A non-transitory computer-readable medium containing instructions that, when executed by a processor, cause the processor to perform a method comprising:

• obtaining a first set of images, wherein each of the first set of images depicts a product;
• obtaining a first set of labels associated with the first set of images, wherein each of the first set of labels corresponds to an image in the first set of images and includes information indicating a mask of the product depicted in the corresponding image;
• training an image segmentation neural network based on the first set of images and the first set of labels;
• obtaining a second set of images, wherein each of the second set of images depicts a known product;
• obtaining a second set of labels associated with the second set of images, wherein each of the second set of labels corresponds to an image in the second set of images and includes information indicating classification information for the known product depicted in the corresponding image;
• training an image classification neural network based on the second set of predetermined images and the second set of labels;
• receiving a query image depicting a product that is not yet identified;
• performing image segmentation of the query image using the image segmentation neural network, wherein performing image segmentation of the query image includes removing background image portions of the query image, thereby obtaining a mask image of the product depicted in the query image;
• and
• identifying the product in the image by performing image analysis of the mask image of the product depicted in the query image using the image classification neural network.