Invention Grant
- Patent Title: Machine learning model development with unsupervised image selection
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Application No.: US16389408Application Date: 2019-04-19
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Publication No.: US10997748B2Publication Date: 2021-05-04
- Inventor: Tyler Staudinger , Zachary D. Jorgensen
- Applicant: THE BOEING COMPANY
- Applicant Address: US IL Chicago
- Assignee: THE BOEING COMPANY
- Current Assignee: THE BOEING COMPANY
- Current Assignee Address: US IL Chicago
- Agency: Womble Bond Dickinson (US) LLP
- Main IPC: G06T7/73
- IPC: G06T7/73 ; G06K9/62 ; G06N3/08

Abstract:
A method of machine learning model development includes receiving a plurality of images of a scene, and performing an unsupervised image selection. This includes applying the images to a pre-trained model to extract and embed the images with respective feature vectors, and performing a cluster analysis to group the images in a clusters based on correlations among the respective feature vectors. The unsupervised image selection also includes selecting at least some but not all images in each of the clusters, and any images considered outliers that belong to none of the clusters, for a subset of the images that includes fewer than all of the images. And the method includes receiving user input to label or labeling objects depicted in the subset of the images to produce a training set of images, and building a machine learning model for object detection using the training set of images.
Public/Granted literature
- US20200334856A1 MACHINE LEARNING MODEL DEVELOPMENT WITH UNSUPERVISED IMAGE SELECTION Public/Granted day:2020-10-22
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