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DCNN과 SVM이 결합된 분류기를 이용하여 소부류 단위로 영상을 분류하는 방법

Title
DCNN과 SVM이 결합된 분류기를 이용하여 소부류 단위로 영상을 분류하는 방법
Other Titles
Fine-grained Image Classification Using Combined DCNN and SVM
Author
문영식
Issue Date
2017-06
Publisher
대한전자공학회
Citation
2017년도 대한전자공학회 하계종합학술대회, Page. 637-640
Abstract
Fine-grained image classification is usually concerned with the same category of objects, for example different kinds of dogs such as Labrador and Golden Retriever. Since the fine-grained categories belong to the same main category, the gap between the categories is very small. These subtle gaps are easily covered by factors such as color, light and background, which leads to the difficulty of fine-grained image classification. In this paper, we propose a dogs breed recognizer that combined deep convolutional neural network with linear support vector machine to tackle fine-grained image classification problems. Deep convolutional neural network is used as a fixed feature extractor, and linear support vector machine is used as a classifier. The proposed method is tested on a dog dataset including 133 dog breeds and 8,351 images. Experimental result demonstrates that the classification accuracy of proposed method outperforms other conventional features methods by over 10 percentage point.
URI
http://www.dbpia.co.kr/Journal/ArticleDetail/NODE07219262http://repository.hanyang.ac.kr/handle/20.500.11754/103358
Appears in Collections:
COLLEGE OF COMPUTING[E] > COMPUTER SCIENCE(소프트웨어학부) > Articles
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