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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳銘憲(Ming-Syan Chen) | |
| dc.contributor.author | David Kao | en |
| dc.contributor.author | 高代維 | zh_TW |
| dc.date.accessioned | 2021-06-17T03:34:14Z | - |
| dc.date.available | 2019-03-01 | |
| dc.date.copyright | 2018-03-01 | |
| dc.date.issued | 2018 | |
| dc.date.submitted | 2018-02-12 | |
| dc.identifier.citation | Wang, Xuesong, et al. 'Zero-shot image classification based on deep feature extraction.' IEEE Transactions on Cognitive and Developmental Systems (2016).
Changpinyo, Soravit, et al. 'Synthesized classifiers for zero-shot learning.' Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016. Norouzi, Mohammad, et al. 'Zero-shot learning by convex combination of semantic embeddings.' arXiv preprint arXiv:1312.5650 (2013). Denton, Emily, et al. 'User conditional hashtag prediction for images.' Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2015. Weston, Jason, Samy Bengio, and Nicolas Usunier. 'Wsabie: Scaling up to large vocabulary image annotation.' IJCAI. Vol. 11. 2011. Frome, Andrea, et al. 'Devise: A deep visual-semantic embedding model.' Advances in neural information processing systems. 2013. Zeiler, Matthew D., and Rob Fergus. 'Visualizing and understanding convolutional networks.' European conference on computer vision. Springer, Cham, 2014. Stutz, David. 'Understanding convolutional neural networks.' InSeminar Report, Fakultät für Mathematik, Informatik und Naturwissenschaften Lehr-und Forschungsgebiet Informatik VIII Computer Vision (2014). He, Kaiming, and Jian Sun. 'Convolutional neural networks at constrained time cost.' Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015. He, Kaiming, et al. 'Deep residual learning for image recognition.' Proceedings of the IEEE conference on computer vision and pattern recognition. 2016. Targ, Sasha, Diogo Almeida, and Kevin Lyman. 'Resnet in Resnet: generalizing residual architectures.' arXiv preprint arXiv:1603.08029 (2016). Simonyan, Karen, and Andrew Zisserman. 'Very deep convolutional networks for large-scale image recognition.' arXiv preprint arXiv:1409.1556 (2014). Szegedy, Christian, et al. 'Going deeper with convolutions.' Proceedings of the IEEE conference on computer vision and pattern recognition. 2015. He, Kaiming, et al. 'Delving deep into rectifiers: Surpassing human-level performance on imagenet classification.' Proceedings of the IEEE international conference on computer vision. 2015. Deng, Jia, et al. 'Imagenet: A large-scale hierarchical image database.' Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. IEEE, 2009. Russakovsky, Olga, et al. 'Imagenet large scale visual recognition challenge.' International Journal of Computer Vision 115.3 (2015): 211-252. Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. 'Imagenet classification with deep convolutional neural networks.' Advances in neural information processing systems. 2012. Zhou, Bolei, et al. 'Places: A 10 million image database for scene recognition.' IEEE transactions on pattern analysis and machine intelligence (2017). Zhou, Bolei, et al. 'Places: An image database for deep scene understanding.' arXiv preprint arXiv:1610.02055 (2016). Zhou, Bolei, et al. 'Learning deep features for scene recognition using places database.' Advances in neural information processing systems. 2014. Xiao, Jianxiong, et al. 'Sun database: Large-scale scene recognition from abbey to zoo.' Computer vision and pattern recognition (CVPR), 2010 IEEE conference on. IEEE, 2010. Everingham, Mark, et al. 'The pascal visual object classes challenge: A retrospective.' International journal of computer vision 111.1 (2015): 98-136. Girshick, Ross, et al. 'Rich feature hierarchies for accurate object detection and semantic segmentation.' Proceedings of the IEEE conference on computer vision and pattern recognition. 2014. Mikolov, Tomas, et al. 'Efficient estimation of word representations in vector space.' arXiv preprint arXiv:1301.3781 (2013). Mikolov, Tomas, et al. 'Distributed representations of words and phrases and their compositionality.' Advances in neural information processing systems. 2013. Bengio, Yoshua, et al. 'A neural probabilistic language model.' Journal of machine learning research 3.Feb (2003): 1137-1155. Mikolov, Tomas, et al. 'Recurrent neural network based language model.' Interspeech. Vol. 2. 2010. Zhang, Hao, et al. 'SVM-KNN: Discriminative nearest neighbor classification for visual category recognition.' Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on. Vol. 2. IEEE, 2006. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/69922 | - |
| dc.description.abstract | 此研究主要是針對 Instagram 社群平台而設計的自動化 Hashtag 推薦系統。當使用者在使用 Instagram 上傳新圖片時，可簡化在操作上與手動標籤 Hashtag 的動作。我們使用了卷積神經網絡 (Convolutional Neural Network) 圖像辨識技術來達成圖像歸類 (image classification) 的效果。但現今，Hashtag 的歸類整理還是個未能被完全解決的問題。社群平台上有著層出不窮和大量的 Hashtag 正在推陳出新，如果只使用單一機器學習方法，將會無法達成完整學習的效果。
在這研究中，我們將會探討不同 Hashtag 的語義程度，而導出並不是所有的 Hashtag 都適合推薦給使用者使用的結論。此外，為了擴充 Hashtag 的詞量，我們結合了圖像辨識和語義嵌入 (semantic embedding) 的模型來建構此推薦系統。透過定期詞彙和語義嵌入模型的更新，此系統將會推薦最符合現今所流行的 Hashtag 詞彙供使用者選擇。我們使用 Instagram 上的圖片做實驗，並完整呈現此推薦系統可以有效的推薦和圖像所呼應的 Hashtag 詞彙。 | zh_TW |
| dc.description.abstract | The goal of this research is to design a system that can predict and recommend hashtags to users when they upload new images on Instagram. The system will simplify the picture tagging process for users by automatically suggesting relevant hashtag options when an image is uploaded. Using the image recognition technology like Convolutional Neural Network (CNN), we could achieve the image classification function. However, hashtag prediction is still an open problem due to the large amount of media contents and hashtag categories; using single machine learning method will not be sufficient.
In this research, we show that not all hashtags are equally meaningful, and some are not suitable in recommendation learning. In addition, we combine image classification and semantic embedding models to gain the expansion of recommended hashtags. At the same time, by periodically updating semantic embedding model, we ensure that the hashtags being recommended follow the latest trends. We apply the design to existing image-hashtag pairs on Instagram and demonstrate that the capability of the system can successfully recommend hashtags that are more relevant to the images. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T03:34:14Z (GMT). No. of bitstreams: 1 ntu-107-R04921100-1.pdf: 4103167 bytes, checksum: 8eca5e03daea1ef8bba87047ea2a196a (MD5) Previous issue date: 2018 | en |
| dc.description.tableofcontents | Acknowledgements ii
摘要 iii Abstract of the Thesis iv List of Figures vii List of Tables ix Chapter 1 - Introduction 1 1.1 The Problem 2 1.2 Structure of the Thesis 4 Chapter 2 - Related Work 5 2.1 Hashtag Prediction for Images 6 2.2 Visual-Semantic Embedding Model 8 Chapter 3 - Preliminaries 10 3.1 Convolutional Neural Network 10 3.1.1 ResNet 12 3.1.2 ZF Net 14 3.2 CNN Models 16 3.2.1 ImageNet 16 3.2.2 Places 18 3.2.3 PASCAL VOC 20 3.3 Semantic Embedding Modeling 22 3.3.1 Word2Vec 23 3.3.2 Skip-gram Model 24 Chapter 4 - Data 27 4.1 Image Dataset 27 4.2 Hashtag Dataset 32 Chapter 5 - Proposed Method 34 5.1 Hashtag Recommendation System 35 Chapter 6 - Experiments and Results 38 6.1 Sampling of Image-Hashtag Pairs 39 6.2 Predicting and Recommending Hashtags 44 6.3 Verification and Inspection 52 Chapter 7 - Conclusion 53 Bibliography 54 | |
| dc.language.iso | en | |
| dc.subject | 卷積神經網路 | zh_TW |
| dc.subject | Skip-gram模型 | zh_TW |
| dc.subject | Word2Vec | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 資料探勘 | zh_TW |
| dc.subject | Hashtag推薦 | zh_TW |
| dc.subject | 語義嵌入 | zh_TW |
| dc.subject | Skip-gram Model | en |
| dc.subject | Data Mining | en |
| dc.subject | Deep Learning | en |
| dc.subject | Convolutional Neural Network (CNN) | en |
| dc.subject | Semantic Embedding Model | en |
| dc.subject | Word2Vec | en |
| dc.subject | Hashtag Recommendation | en |
| dc.title | 使用深度學習之Hashtag推薦系統 | zh_TW |
| dc.title | Hashtag Recommendation using Deep Neural Networks | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 106-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 王鈺強(Yu-Chiang Wang),王奕翔(I-Hsiang Wang),陳怡伶(Yi-Ling Chen) | |
| dc.subject.keyword | Hashtag推薦,資料探勘,深度學習,卷積神經網路,語義嵌入,Word2Vec,Skip-gram模型, | zh_TW |
| dc.subject.keyword | Hashtag Recommendation,Data Mining,Deep Learning,Convolutional Neural Network (CNN),Semantic Embedding Model,Word2Vec,Skip-gram Model, | en |
| dc.relation.page | 56 | |
| dc.identifier.doi | 10.6342/NTU201800488 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2018-02-13 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| 顯示於系所單位： | 電機工程學系 | |
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