IP Library › Granted Patent US 11,106,942
Granted Patent B2
US 11,106,942 · App. 16/696,354 · Granted Aug 31, 2021

Method and apparatus for generating learning data required to learn animation characters based on deep learning

Inventors: Dong Hyuck Im (Daejeon, KR); Jung Hyun Kim (Daejeon, KR); Hye Mi Kim (Daejeon, KR); Jee Hyun Park (Daejeon, KR); Yong Seok Seo (Daejeon, KR); Won Young Yoo (Daejeon, KR)
Assignee: ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE
G06K9/6259G06K9/00362G06K9/00718G06K9/00744G06K9/2054G06K9/42G06K9/6218G06K9/6253G06K9/6257G06N20/00
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Quick Facts
Patent No.
US 11,106,942
App. No.
16/696,354
Filed
Nov 26, 2019
Granted
Aug 31, 2021
Kind
B2
Art Unit
2637
USPC
382/155
Abstract

Disclosed are a learning data generation method and apparatus needed to learn animation characters on the basis of deep learning. The learning data generation method needed to learn animation characters on the basis of deep learning may include collecting various images from an external source using wired/wireless communication, acquiring character images from the collected images using a character detection module, clustering the acquired character images, selecting learning data from among the clustered images, and inputting the selected learning data to an artificial neural network for character recognition.

Claims (56)

1. A learning data generation method to learn animation characters on the basis of deep learning, the learning data generation method comprising:

collecting various images from an external source using wired/wireless communication;

training a character detection module using the collected images;

acquiring character images from the collected images using the trained character detection module;

clustering the acquired character images;

selecting learning data from among the clustered images; and

inputting the selected learning data to an artificial neural network for character recognition,

wherein the training of the character detection module comprises:

labeling the collected images to generate labels corresponding to the respective images; and

inputting the generated labels and the collected images to a preset character detection model to train the character detection model.

2. The learning data generation method of claim 1 , wherein the collecting of the various images comprises:

collecting a video from the external source using the wired/wireless communication; and

extracting frames from the collected video at preset time intervals.

3. The learning data generation method of claim 1 , wherein each of the labels is a label conforming to DarkNet format and includes a class number, a start point coordinate ratio of a bounding box, and an aspect ratio of the bounding box.

4. The learning data generation method of claim 1 , wherein the acquiring of the character images comprises:

detecting a bounding-box-shaped character region from each of the collected images using the trained character detection model; and

preprocessing the detected character region to acquire the corresponding character image.

5. The learning data generation method of claim 4 , wherein the preprocessing comprises cutting the character region out of the corresponding collected image and normalizing the size of the cut character region.

6. The learning data generation method of claim 1 , wherein the clustering comprises:

extracting a feature vector from the character image; and

clustering the character image using the extracted feature vector.

7. The learning data generation method of claim 6 , wherein the clustering of the character image using the extracted feature vector comprises:

clustering the character image;

adjusting a cluster determination strength value according to the number of clusters generated by the clustering; and

re-clustering the character image.

8. The learning data generation method of claim 7 , wherein the adjusting of the cluster determination strength value comprises:

increasing the cluster determination strength value when the number of clusters exceeds a maximal limit value within a preset limit range; and

decreasing the cluster determination strength value when the number of clusters is less than an initial limit value within the limit range.

9. A learning data generation apparatus needed to learn animation characters on the basis of deep learning, the learning data generation apparatus comprising:

at least one processor; and

a memory configured to store instructions for instructing the at least one processor to perform at least one operation,

wherein the at least one operation comprises:

collecting various images from an external source using wired/wireless communication;

training a character detection module using the collected images;

acquiring character images from the collected images using the trained character detection module;

clustering the acquired character images;

selecting learning data from among the clustered images; and

inputting the selected learning data to an artificial neural network for character recognition,

wherein the training of the character detection module comprises:

labeling the collected images to generate labels corresponding to the respective images; and

inputting the generated labels and the collected images to a preset character detection model to train the character detection model.

10. The learning data generation apparatus of claim 9 , wherein the collecting of the various images comprises:

collecting a video from the external source using the wired/wireless communication; and

extracting frames from the collected video at preset time intervals.

11. The learning data generation apparatus of claim 9 , wherein the character detection model is one of You-Only-Look-Once (YOLO), Single Shot Detector (SSD), and Faster R-convolutional neural network (CNN).

12. The learning data generation apparatus of claim 9 , wherein each of the labels is a label conforming to DarkNet format and includes a class number, a start point coordinate ratio of a bounding box, and an aspect ratio of the bounding box.

13. The learning data generation apparatus of claim 9 , wherein the acquiring of the character images comprises:

detecting a bounding-box-shaped character region from the collected image using the trained character detection model; and

preprocessing the detected character region to acquire the character image.

14. The learning data generation apparatus of claim 13 , wherein the preprocessing comprises cutting the character region out of the corresponding collected image and normalizing the size of the cut character region.

15. The learning data generation apparatus of claim 9 , wherein the clustering comprises:

extracting a feature vector from the character image; and

clustering the character image using the extracted feature vector.

16. The learning data generation apparatus of claim 9 , wherein the selecting of the learning data comprises:

displaying, to a user, representative images for a plurality of clusters to which the clustered image belongs; and

selecting, as the learning data, a character image belonging to a cluster corresponding to an image selected by the user from among the displayed representative images.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Nov 27, 2019
From: IM, DONG HYUCK; KIM, JUNG HYUN; KIM, HYE MI; PARK, JEE HYUN; SEO, YONG SEOK; YOO, WON YOUNG
To: ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE
Reel/Frame 051127/0246 →
Priority Claims (1)
KR 10-2019-0124592 · Oct 8, 2019 · national
Continuity (1)
Related Publication 20210103721A1 · Apr 8, 2021