DCNN과 SVM이 결합된 분류기를 이용하여 소부류 단위로 영상을 분류하는 방법
- DCNN과 SVM이 결합된 분류기를 이용하여 소부류 단위로 영상을 분류하는 방법
- Other Titles
- Fine-grained Image Classification Using Combined DCNN and SVM
- Issue Date
- 2017년도 대한전자공학회 하계종합학술대회, Page. 637-640
- 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.
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- COLLEGE OF COMPUTING[E] > COMPUTER SCIENCE(소프트웨어학부) > Articles
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