Transfer learning based on different image retrieval models
With the rapid development of digital technology, image retrieval has been used in more and more applications; for example, commodity retrieval, scenic spot retrieval, etc. However, the difficulty of collecting different types of images varies from cases to cases. Some images are easy to collect and...
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| Format: | Thesis-Master by Coursework |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/163989 |
| _version_ | 1826123551799771136 |
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| author | Cai, Qiong |
| author2 | Tan Yap Peng |
| author_facet | Tan Yap Peng Cai, Qiong |
| author_sort | Cai, Qiong |
| collection | NTU |
| description | With the rapid development of digital technology, image retrieval has been used in more and more applications; for example, commodity retrieval, scenic spot retrieval, etc. However, the difficulty of collecting different types of images varies from cases to cases. Some images are easy to collect and there are large amounts of data for model training. On the other hand, some images are difficult to come by and it is not easy to have enough data for model training. Under such situation, transfer learning can be applied to address partly the problem of insufficient training set. In this dissertation, we will use two datasets (the animal dataset and the Pokemon dataset) to perform transfer learning on three image retrieval models which build on lightweight convolution network, heavyweight convolution network, and U-Net, respectively. The mAP (mean
average precision) is used as the evaluation indicator to explore the effect of transfer learning for these three models. Through experiments, we conclude that the effect of model-based transfer learning on image retrieval model based on lightweight convolutional neural network is more profound.
Keywords: Auto-Encoder, Convolution Neural Network, U-Net, Transfer Learning, Image Retrieval. |
| first_indexed | 2024-10-01T06:06:32Z |
| format | Thesis-Master by Coursework |
| id | ntu-10356/163989 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:06:32Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1639892022-12-29T09:23:11Z Transfer learning based on different image retrieval models Cai, Qiong Tan Yap Peng School of Electrical and Electronic Engineering EYPTan@ntu.edu.sg Engineering::Electrical and electronic engineering::Computer hardware, software and systems With the rapid development of digital technology, image retrieval has been used in more and more applications; for example, commodity retrieval, scenic spot retrieval, etc. However, the difficulty of collecting different types of images varies from cases to cases. Some images are easy to collect and there are large amounts of data for model training. On the other hand, some images are difficult to come by and it is not easy to have enough data for model training. Under such situation, transfer learning can be applied to address partly the problem of insufficient training set. In this dissertation, we will use two datasets (the animal dataset and the Pokemon dataset) to perform transfer learning on three image retrieval models which build on lightweight convolution network, heavyweight convolution network, and U-Net, respectively. The mAP (mean average precision) is used as the evaluation indicator to explore the effect of transfer learning for these three models. Through experiments, we conclude that the effect of model-based transfer learning on image retrieval model based on lightweight convolutional neural network is more profound. Keywords: Auto-Encoder, Convolution Neural Network, U-Net, Transfer Learning, Image Retrieval. Master of Science (Computer Control and Automation) 2022-12-29T09:23:11Z 2022-12-29T09:23:11Z 2022 Thesis-Master by Coursework Cai, Q. (2022). Transfer learning based on different image retrieval models. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/163989 https://hdl.handle.net/10356/163989 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering::Computer hardware, software and systems Cai, Qiong Transfer learning based on different image retrieval models |
| title | Transfer learning based on different image retrieval models |
| title_full | Transfer learning based on different image retrieval models |
| title_fullStr | Transfer learning based on different image retrieval models |
| title_full_unstemmed | Transfer learning based on different image retrieval models |
| title_short | Transfer learning based on different image retrieval models |
| title_sort | transfer learning based on different image retrieval models |
| topic | Engineering::Electrical and electronic engineering::Computer hardware, software and systems |
| url | https://hdl.handle.net/10356/163989 |
| work_keys_str_mv | AT caiqiong transferlearningbasedondifferentimageretrievalmodels |