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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2020.4
- 수록면
- 525 - 538 (14page)
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초록· 키워드
오류제보하기
A practical animal face classification system that classifies animals in image and video data is considered as a pivotal topic in machine learning. In this research, we are proposing a novel method of fully connected dual Deep Convolutional Neural Network (DCNN), which extracts and analyzes image features on a large scale. With the inclusion of the state of the art Batch Normalization layer and Exponential Linear Unit (ELU) layer, our proposed DCNN has gained the capability of analyzing a large amount of dataset as well as extracting more features than before. For this research, we have built our dataset containing ten thousand animal faces of ten animal classes and a dual DCNN. The significance of our network is that it has four sets of convolutional functions that work laterally with each other. We used a relatively small amount of batch size and a large number of iteration to mitigate overfitting during the training session. We have also used image augmentation to vary the shapes of the training images for the better learning process. The results demonstrate that, with an accuracy rate of 92.0%, the proposed DCNN outruns its counterparts while causing less computing costs.
목차
ABSTRACT
1. INTRODUCTION
2. RELATED WORKS
3. PROPOSED METHOD
4. DATASET, EXPERIMENTS, AND RESULTS
5. CONCLUSION
REFERENCE
참고문헌 (11)
참고문헌 신청
S. Taheri, O. Toygar, “Animal classification using facial images with score-level fusion,” IET Computer Vision, Vol. 12, Issue 5, pp. 679- 685, Aug. 2018.
A. Krizhevsky, I. Sutskever, and G. Hinton, “Imagenet Classification with Deep Convolutional Neural Networks,” Advances in Neural Information Processing Systems, pp. 1106- 1114, 2012.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going Deeper with Convolutions,” IEEE Conference on Computer Vision and Pattern Recognition, Vol: 1, Pages: 1-9, 2015.
K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-scale Image Recognition,” arXiv preprint arXiv: 1409.1556, 2014.
T. Trnovszky, P. Kamencay, R. Orjesek, M. Benco, and P. Sykora, “Animal Recognition System Based on Convolutional Neural Network,” Advances in Electrical and Electronic Engineering, Vol. 15, No. 3, Sept. 2017.
A. Krizhevsky, I. Sutskever, and G. Hinton, “Imagenet Classification with Deep Convolutional Neural Networks,” Advances in Neural Information Processing Systems, pp. 1106- 1114, 2012.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going Deeper with Convolutions,” IEEE Conference on Computer Vision and Pattern Recognition, Vol: 1, Pages: 1-9, 2015.
K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-scale Image Recognition,” arXiv preprint arXiv: 1409.1556, 2014.
T. Trnovszky, P. Kamencay, R. Orjesek, M. Benco, and P. Sykora, “Animal Recognition System Based on Convolutional Neural Network,” Advances in Electrical and Electronic Engineering, Vol. 15, No. 3, Sept. 2017.
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UCI(KEPA) : I410-ECN-0101-2020-004-000580403