US 11,721,091 B2
Clustering historical images using a convolutional neural net and labeled data bootstrapping
Laryn Brown, Highland, UT (US); Michael Murdock, Lehi, UT (US); Jack Reese, Lindon, UT (US); and Shawn Reid, Orem, UT (US)
Assigned to Ancestry.com Operations Inc., Lehi, UT (US)
Filed by Ancestry.com Operations Inc., Lehi, UT (US)
Filed on Jan. 26, 2021, as Appl. No. 17/158,801.
Application 17/158,801 is a continuation of application No. 16/397,114, filed on Apr. 29, 2019, granted, now 10,943,146.
Application 16/397,114 is a continuation of application No. 15/393,008, filed on Dec. 28, 2016, granted, now 10,318,846, issued on Jun. 11, 2019.
Prior Publication US 2021/0150262 A1, May 20, 2021
This patent is subject to a terminal disclaimer.
Int. Cl. G06V 10/82 (2022.01); G06F 18/40 (2023.01); G06V 10/778 (2022.01)
CPC G06V 10/82 (2022.01) [G06F 18/41 (2023.01); G06V 10/7788 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A method of using pre-trained neural networks to train classifiers to classify images, the method comprising:
providing a plurality of genealogical images to an image classifier; and
for a genealogical image of the plurality of genealogical images:
creating a feature vector for the genealogical image using a feature extractor of the image classifier, wherein the feature extractor includes a pre-trained neural network previously trained to extract feature vectors from images within a non-genealogical image database;
based on the feature vector created using the feature extractor trained on the non-genealogical image database, assigning a label to the genealogical image using a feature classifier of the image classifier, wherein the feature classifier is separate from the feature extractor and the pre-trained neural network;
receiving a corrected label for the genealogical image;
determining an error between the label and the corrected label; and
adjusting the feature classifier to improve classification of genealogical images based on the error without adjustment to the feature extractor and the pre-trained neural network.