현재 위치
Home
Comparison of Fine-Tuned Convolutional Neural Networks for Clipart Style Classification
-
- 발행기관
- 국제인공지능학회(구 한국인터넷방송통신학회) 바로가기
-
- 간행물
- International Journal of Internet, Broadcasting and Communication KCI 등재후보 바로가기
-
- 통권
- Vol.9 No.4 (2017.11)바로가기
-
- 페이지
- pp.1-7
-
- 저자
-
- 언어
- 영어(ENG)
-
- URL
- https://www.earticle.net/Article/A315391
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
- 영어
- Clipart is artificial visual contents that are created using various tools such as Illustrator to highlight some information. Here, the style of the clipart plays a critical role in determining how it looks. However, previous studies on clipart are focused only on the object recognition [16], segmentation, and retrieval of clipart images using hand-craft image features. Recently, some clipart classification researches based on the style similarity using CNN have been proposed, however, they have used different CNN-models and experimented with different benchmark dataset so that it is very hard to compare their performances. This paper presents an experimental analysis of the clipart classification based on the style similarity with two well-known CNN-models (Inception Resnet V2 [13] and VGG-16 [14] and transfers learning with the same benchmark dataset (Microsoft Style Dataset 3.6K). From this experiment, we find out that the accuracy of Inception Resnet V2 is better than VGG for clipart style classification because of its deep nature and convolution map with various sizes in parallel. We also find out that the end-to-end training can improve the accuracy more than 20% in both CNN models.
목차
Abstract
1. Introduction
2. Related works
2.1 Similarity research using hand-craft visual features
2.2 Similarity research using convolutional neural network
3. Clipart style classification
3.1 Benchmark Dataset
3.2 CNN for illustration style classification
4. Experiments
5. Conclusions
Acknowledgement
References
키워드
Clipart Classification Convolutional neural network Computer vision Clipart style search Finetuning Deep learning저자
간행물 정보
발행기관
-
- 발행기관명
- 국제인공지능학회(구 한국인터넷방송통신학회) [The International Association for Artificial Intelligence]
- 설립연도
- 2000
- 분야
- 공학>전자/정보통신공학
- 소개
- 인터넷방송, 인터넷 TV , 방송 통신 네트워크 및 관련 분야에 대한 국내는 물론 국제적인 학술, 기술의 진흥발전에 공헌하고 지식 정보화 사회에 기여하고자 한다.
간행물
-
- 간행물명
- International Journal of Internet, Broadcasting and Communication
- 간기
- 계간
- pISSN
- 2288-4920
- eISSN
- 2288-4939
- 수록기간
- 2009~2025
- 십진분류
- KDC 326 DDC 380
이 권호 내 다른 논문 / International Journal of Internet, Broadcasting and Communication Vol.9 No.4
피인용수 : 0건 (자료제공 : 네이버학술정보)
함께 이용한 논문 이 논문을 다운로드한 분들이 이용한 다른 논문입니다.
출처 : 네이버학술정보
0개의 논문이 장바구니에 담겼습니다.