Automatic image selection for online product catalogs

Patent metadata

Field	Value
Publication number	US-11699101-B2
Application number	US-202117467170-A
Country	US
Kind code	B2
Filing date	Sep 3, 2021
Priority date	Sep 10, 2019
Publication date	Jul 11, 2023
Grant date	Jul 11, 2023

Abstract

Disclosed are systems, methods, and non-transitory computer-readable media for automatic image selection for online product catalogs. An image selection system gathers feature data for images of an item included in listings posted to an online marketplace. The image selection system uses the feature data as input in a machine learning model to determine probability scores indicating an estimated probability that each image is suitable to represent the item. The machine learning model is trained based on a set of training images of the item that have been labeled to indicate whether they are suitable to represent the image. The image selection system compares the probability scores and selects an image to represent the item as a stock image based on the comparison.

First claim

What is claimed is:

1. A method performed by one or more computer processors, comprising:
gathering a first set of feature data for a first image of a first item, the first set of feature data including image features describing the first image;
determining a first probability score for the first image by using the first set of feature data as input in a machine learning model, the first probability score indicating an estimated probability that the first image is suitable to represent the first item, the machine learning model having been trained to predict an image for a listing of an online marketplace based on a set of training images of the first item and sets of feature data associated with the set of training images;
gathering a second set of feature data for a second image of the first item, the second set of feature data including image features describing the second image;
determining a second probability score for the second image by using the second set of feature data as input in the machine learning model, the second probability score indicating an estimated probability that the second image is suitable to represent the first item; and
selecting, based on a comparison of at least the first probability score and the second probability score, the first image to represent the first item.

2. The method of claim 1 , wherein selecting the first image to represent the first item comprises:
determining, based on the comparison of at least the first probability score and the second probability score, that the first probability score is greater than the second probability score.

3. The method of claim 1 , wherein the first set of feature data for the first image also includes item features describing the first item.

4. The method of claim 1 , wherein the first set of feature data for the first image also includes categorical features describing a category of items in which the first item is included.

5. The method of claim 1 , wherein each training image from the set of training images is labeled to indicate whether a respective training image is suitable to represent the first item.

6. The method of claim 1 , further comprising:
gathering, a third set of feature data for a third image of the first item, the third set of feature data including image features describing the third image; and
determining a third probability score for the third image by using the third set of feature data as input in the machine learning model, the third probability score indicating on estimated probability that the third image is suitable to represent the first item, wherein the comparison of at least the first probability score and the second probability score also includes the third probability score.

7. The method of claim 1 , wherein the first image is selected to represent the first item by representing a category of items that encompasses the first item.

8. A system comprising:
one or more computer processors; and
one or more computer-readable mediums storing instructions that, when executed by the one or more computer processors, cause the system to perform operations comprising:
gathering a first set of feature data for a first image of a first item, the first set of feature data including image features describing the first image;
determining a first probability score for the first image by using the first set of feature data as input in a machine learning model, the first probability score indicating an estimated probability that the first image is suitable to represent the first item, the machine learning model having been trained to predict an image for a listing of an online marketplace based on a set of training images of the first item and sets of feature data associated with the set of training images;
gathering a second set of feature data for a second image of the first item, the second set of feature data including image features describing the second image;
determining a second probability score for the second image by using the second set of feature data as input in the machine learning model, the second probability score indicating an estimated probability that the second image is suitable to represent the first item; and
selecting, based on a comparison of at least the first probability score and the second probability score, the first image to represent the first item.

9. The system of claim 8 , wherein selecting the first image to represent the first item comprises:
determining, based on the comparison of at least the first probability score and the second probability score, that the first probability score is greater than the second probability score.

10. The system of claim 8 , wherein the first set of feature data for the first image also includes item features describing the first item.

11. The system of claim 8 , wherein the first set of feature data for the first image also includes categorical features describing a category of items in which the first item is included.

12. The system of claim 8 , wherein each training image from the set of training images is labeled to indicate whether a respective training image is suitable to represent the first item.

13. The system of claim 8 , the operations further comprising:
gathering, a third set of feature data for a third image of the first item, the third set of feature data including image features describing the third image; and
determining a third probability score for the third image by using the third set of feature data as input in the machine learning model, the third probability score indicating on estimated probability that the third image is suitable to represent the first item, wherein the comparison of at least the first probability score and the second probability score also includes the third probability score.

14. The system of claim 8 , wherein the first image is selected to represent the first item by representing a category of items that encompasses the first item.

15. A non-transitory computer-readable medium storing instructions that, when executed by one or more computer processors of one or more computing devices, cause the one or more computing devices to perform operations comprising:
gathering a first set of feature data for a first image of a first item, the first set of feature data including image features describing the first image;
determining a first probability score for the first image by using the first set of feature data as input in a machine learning model, the first probability score indicating an estimated probability that the first image is suitable to represent the first item, the machine learning model having been trained to predict an image for a listing of an online marketplace based on a set of training images of the first item and sets of feature data associated with the set of training images;
gathering a second set of feature data for a second image of the first item, the second set of feature data including image features describing the second image;
determining a second probability score for the second image by using the second set of feature data as input in the machine learning model, the second probability score indicating an estimated probability that the second image is suitable to represent the first item; and
selecting, based on a comparison of at least the first probability score and the second probability score, the first image to represent the first item.

16. The non-transitory computer-readable medium of claim 15 , wherein selecting the first image to represent the first item comprises:
determining, based on the comparison of at least the first probabilit

Assignees

• Ebay Inc

Inventors

• Dagan Arnon
• Guy Ido
• Nus Alexander
• Bryl Raphael
• Barzilai Noa Shimoni
• Omer Avinoam
• Radovilsky Yan
• Itamar Einav
• Mikles Gadi

Classifications

• G06V10/764

  using classification, e.g. of video objects · CPC title

• G06V20/35

  Categorising the entire scene, e.g. birthday party or wedding scene · CPC title

• G06F18/2178

  based on feedback of a supervisor · CPC title

• G06F18/2148

  characterised by the process organisation or structure, e.g. boosting cascade · CPC title

• G06N20/00 Primary

  Machine learning · CPC title