| Title |
A Methodology of Enhancing the Accuracy of Image Classification with CNN |
| Authors |
이상현(Lee, Sang-Hyun) ; 노녕(Lu, Ning) |
| DOI |
https://doi.org/10.5659/JAIK.2020.36.9.15 |
| Keywords |
CNN; Artificial Neural Network; Image Classification; XOR; Deep Learning |
| Abstract |
This research proposes the method of using the Convolutional Neural Network (CNN) more efficiently in the classification of architectural
images. The characteristics of images are not considered in the conventional methods of classifying architectural images. In this research,
architectural images are first categorized primarily as two-dimensional images such as facades or three-dimensional images such as
perspectives before classifying the types of the images. We compared the cases, where the types of architectural images are classified without
being categorized as two- or three-dimensional images, with the cases where they are classified after the preliminary categorization is
conducted. In this research, it was confirmed that the latter had higher classification accuracy. This implies that for image classification using
the CNN, it could be effective to classify the types of architectural images after categorizing the basic preliminary delimitation based on
characteristics of the images. We anticipate that the outcome of this research for architectural images will also apply to classification of other
types of images. |