US 11,886,543 B2Granted

Interactive iterative image annotation

AssigneeKoninklijke Philips N.V.

InventorsJuergen Weese, Thomas Blaffert, Tom Brosch, Hans Barschdorf

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Abstract

A system and computer-implemented method are provided for annotation of image data. A user is enabled to iteratively annotate the image data. An iteration of said iterative annotation comprises generating labels for a current image data part based on user-verified labels of a previous image data part, and enabling the user to verify and correct said generated labels to obtain user-verified labels for the current image data part. The labels for the current image data part are generated by combining respective outputs of a label propagation algorithm and a machine-learned classifier trained on user-verified labels and image data and applied to image data of the current image data part. The machine-learned classifier is retrained using the user-verified labels and the image data of the current image data part to obtain a retrained machine-learned classifier.

Claims20

Dependent claims

A system for annotation of image data, the system comprising:

an input interface configured to access the image data to be annotated;
a user interface subsystem comprising:
a user input interface configured to receive user input data from a user input device operable by a user;
a display output interface configured to provide display data to a display to visualize output of the system;
a processor configured to, using the user interface subsystem, establish a user interface which enables the user to iteratively annotate the image data, wherein an iteration of said iterative annotation comprises:
the processor generating labels for a current image data part based on user-verified labels of a previous image data part;
via the user interface, enabling the user to verify and correct said generated labels to obtain user-verified labels for the current image data part;
wherein the processor is further configured to:
generate the labels for the current image data part by combining, by weighting, respective outputs of:
a label propagation algorithm which propagates the user-verified labels of the previous image data part to the current image data part, and
a machine-learned classifier for labeling of image data, wherein the machine-learned classifier is trained on user-verified labels and image data and applied to image data of the current image data part;
and
retrain the machine-learned classifier using the user-verified labels and the image data of the current image data part to obtain a retrained machine-learned classifier.

10 more elements

Dependencies are inferred from the claim text.

Facts

Application: 17/294,731Filed 15 Nov 2019
Priority: EP 1820789622 Nov 2018
Published: US 2022/0019860 A120 Jan 2022
Granted: 30 Jan 2024
Term ends: Not estimatedThe source records don’t support an estimate.

Classification14 codes

G06F 18/2178
Validation; Performance evaluation; Active pattern learning techniques: based on feedback of a supervisor
G16H 50/20
ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or…: for…
G06F 18/2148
Generating training patterns; Bootstrap methods, e.g. bagging or boosting: characterised by the process organisation or structure, e.g. boosting…
G06F 18/40
Pattern recognition: software arrangements specially adapted for pattern recognition, e.g. user interfaces or toolboxes therefor