Open-set pattern recognition in computer vision
In traditional classification or recognition tasks in the field of Computer Vision, people assume that all test sample classes are first encountered during training, which is unrealistic. Open Set Recognition aims to reject unknown classes and correctly classify known classes during testing. This...
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| 格式: | Thesis-Master by Coursework |
| 語言: | English |
| 出版: |
Nanyang Technological University
2024
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/178226 |
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| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | In traditional classification or recognition tasks in the field of Computer Vision, people assume
that all test sample classes are first encountered during training, which is unrealistic. Open Set
Recognition aims to reject unknown classes and correctly classify known classes during testing. This project aims in studying and implementing the application of Open Set Recognition
technology in image classification tasks. This paper first reviews the proposal and development
of Open Set Recognition technology, and then focuses on the study of five classic Open Set
Recognition algorithms. Based on self-defined convolutional neural networks, this article
attempts to replicate the algorithms of four of these methods. We conducted experiments on these
four algorithms based on mainstream open dataset settings. The results indicate that compared to
traditional deep neural network based classification algorithms as the baseline, the four
algorithms studied in this dissertation have successfully reduced open space risks and achieved
obvious advantages in performance in open set image recognition tasks. |
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