JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
Semantic Image Segmentation for Efficiently Adding Recognition Objects |
| 영문제목(English Title) |
Semantic Image Segmentation for Efficiently Adding Recognition Objects |
| 저자(Author) |
Chengnan Lu
Jinho Park
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| 원문수록처(Citation) |
VOL 18 NO. 05 PP. 0701 ~ 0710 (2022. 10) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
With the development of artificial intelligence technology, various methods have been developed for recognizing objects in images using machine learning. Image segmentation is the most effective among these methods for recognizing objects within an image. Conventionally, image datasets of various classes are trained simultaneously. In situations where several classes require segmentation, all datasets have to be trained thoroughly. Such repeated training results in low training efficiency because most of the classes have already been trained. In addition, the number of classes that appear in the datasets affects training. Some classes appear in datasets in remarkably smaller numbers than others, and hence, the training errors will not be properly reflected when all the classes are trained simultaneously. Therefore, a new method that separates some classes from the dataset is proposed to improve efficiency during training. In addition, the accuracies of the conventional and proposed methods are compared. |
| 키워드(Keyword) |
Image Segmentation
Machine Learning
Object Detection
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| 파일첨부 |
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