Abstract: A product description generating system dynamically generates a description of an item based on one or more images. The product description generating system includes an attribute machine learning system that receives images of the item and determines at least one attribute value and corresponding prediction confidence value based on the image data. The user review interface provides an interface for reviewing attribute values generated by the attribute machine learning system. If the prediction confidence value is outside a threshold, then the attribute value is reviewed by a user via the user review interface. If, on the other hand, the prediction confidence value is within a threshold, then the attribute value is used to generate the description without user review. The user review interface groups items to be reviewed by taxon and facilitates user review of many items at scale.

Abstract: A color correction system comprises a correction information generator and an image processor. The correction information generator generates color correction information from a color correction image associated with a plurality of images. The image processor automatically processes the plurality of images based on the color correction information to generate a plurality of color-corrected images. In operation, images and a color correction image associated with the images are received. Next, color correction information is generated from the color correction image. For example, color correction information includes a color correction matrix or white balance parameters. Next, the images are automatically processed based on the color correction information thereby generating color-corrected images. The system automates color correction processing of studio images to provide highly scalable and reliable color correction.