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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 劉力瑜 | |
dc.contributor.author | Hao-Yu Chuang | en |
dc.contributor.author | 莊皓宇 | zh_TW |
dc.date.accessioned | 2021-05-19T17:47:16Z | - |
dc.date.available | 2023-06-21 | |
dc.date.available | 2021-05-19T17:47:16Z | - |
dc.date.copyright | 2018-06-21 | |
dc.date.issued | 2018 | |
dc.date.submitted | 2018-06-14 | |
dc.identifier.citation | [1] Chang, C. I. (1999). Spectral Information Divergence for Hyperspectral Image Analysis. Geoscience and Remote Sensing Symposium. 1,509-511.
[2] Chapelle, O., Haffner , P., Vapnik, V. (1999). SVMs for Histogram-Based Image Classification, IEEE Transactions on Neural Networks. 10(5), 1055 – 1064. [3] Chen, K., Li, R., Dou, Yong., Liang, Z., Lv, Qi. (2017). Ranking Support Vector Machine with Kernel Approximation. Computational Intelligence and Neuroscience, Volume 2017, Article ID 4629534, 9 pages. [4] Chen, Y., Zhou, X. S., Huang, T. S. (2001). ONE-CLASS SVM FOR LEARNING IN IMAGE RETRIEVAL. Proceedings 2001 International Conference on Image Processing, 3, 34-37. [5] Chiroma, H., Abdulkareem, S., Abubakar, A. I., Herawan, T. (2014). Kernel Functions for the Support Vector Machine: Comparing Performances on Crude Oil Price Data. Recent Advances on Soft Computing and Data Mining, 287, 273-281. [6] Chu, W. T., Li, W. W. (2017). Manga Face Net: Face Detection in Manga based on Deep Neural Network. Proceedings of ACM International Conference on Multimedia Retrieval, 412-415. [7] Guo, G., Li, S. Z., Chan , K. (2000). Face Recognition by Support Vector Machines. Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition. [8] Hsu, C.-W., & Lin, C.J. (2002). A Comparison of Methods for Multiclass Support Vector Machines, 13(2), 415-425. [9] Jin, Y., Zhang, J., Li, M., Tian, Y., Zhu, H., Fang, Z. (2017). Towards the Automatic Anime Characters Creation with Generative Adversarial Networks. arXiv.org, 1708.05509. [10] Liu, Z., & Xu, H. (2014). Kernel Parameter Selection for Support Vector Machine Classification. Journal of Algorithms & Computational Technology, 8(2), 163-177. [11] Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., Leisch, F. (2017). e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien. https://CRAN.R-project.org/package=e1071. [12] Mercier, G., & Lennon, M. (2003). Support Vector Machines for Hyperspectral Image Classification with Spectral-based kernels. Geoscience and Remote Sensing Symposium, 1, 288-290. [13] Moughal, T. A. (2013). Hyperspectral image classification using Support Vector Machine. Journal of Physics: Conference Series, 439(1), 12-42. [14] Osuna, E., Freund, R., Girosit, F. (1997). Training support vector machines: an application to face detection. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Osuna, 130-136. [15] Takayama, K., Johan, H., Nishita, T. (2012). Face detection and face recognition of cartoon characters using feature extraction. Processing of the IEEEJ Image Electronics and Visual Computing Workshop. [16] Siddiqui, K. T. A., & Wasif , A.(2015). International Journal on Soft Computing, 6(1), 37-52. [17] Vapnik, V., & Cortes, C. (1995). Support-Vector Networks. Machine Learning, 20(3), 273-297. [18] 一般社団法人日本動画協会(2016). アニメ産業レポート. [19] 林宗勳. Support Vector Machines 簡介. (http://www.cmlab.csie.ntu.edu.tw/~cyy/learning/tutorials/SVM2.pdf) | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/7590 | - |
dc.description.abstract | 內容圖像檢索,是將圖片中的像素資料作為圖像搜尋依據,在圖片搜尋引擎中,依照提供的圖像找到相似的圖像,提供使用者從圖像資料庫找尋所需要的圖片,在像素的資料處理上,常會遇到變數資料過多樣本數過少之問題,如能利用對應之圖像分類統計演算法,可提升圖像搜尋準確度。
本研究使用支持向量機分類方法,將 30 位動漫人物圖像進行分類,從中比較核函數在多元分類與二元分類上校正後之準確率與執行效率,期望使用較少的像素資料與參數較少的分類法則作為動漫圖像分類的演算法。多元分類的部分,透過校正 3 種核函數後,使用 radial basis 核函數的非線性支持向量機分類方法比最鄰近分類法在動漫圖像多元分類較穩健,在二元分類情境下,線性支持向量機分類方法就能達到準確的分類效果,可依此做為日後圖庫搜尋系統之參考依據。 | zh_TW |
dc.description.provenance | Made available in DSpace on 2021-05-19T17:47:16Z (GMT). No. of bitstreams: 1 ntu-107-R05621201-1.pdf: 1487399 bytes, checksum: f2686a39f51c97be36707c81d3c2f6f0 (MD5) Previous issue date: 2018 | en |
dc.description.tableofcontents | 誌謝 i
摘要 ii Abstract iii 表目錄 1 圖目錄 1 第一章圖像蒐集 1 1.1臉部及雙眼擷取 1 1.2圖像資料處理 3 第二章統計分析方法 4 2.1支持向量機簡介 4 2.1分類方法 7 第三章結果與討論 9 3.1多張圖片分類 9 3.1.1參數校正 9 3.1.2模型預測結果與討論 20 3.2二元圖片分類 21 3.2.1參數校正 21 3.2.2模型預測結果與討論 28 第四章總結 30 參考文獻 31 附錄一 33 公式推導 33 支持向量機公式 33 懲罰權重公式 35 附錄二 36 支持向量機與KNN比較 36 | |
dc.language.iso | zh-TW | |
dc.title | 應用支持向量機於動漫圖像分類 | zh_TW |
dc.title | Apply support vector machine in anime images classification. | en |
dc.type | Thesis | |
dc.date.schoolyear | 106-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 黃信誠,蔡欣甫 | |
dc.subject.keyword | 支持向量機,多元分類,二元分類,圖像搜尋,核函數, | zh_TW |
dc.subject.keyword | image retrieval,support vector machine,mutiple categories,binary categories,kernel, | en |
dc.relation.page | 36 | |
dc.identifier.doi | 10.6342/NTU201800947 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2018-06-14 | |
dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
dc.contributor.author-dept | 農藝學研究所 | zh_TW |
dc.date.embargo-lift | 2023-06-21 | - |
顯示於系所單位: | 農藝學系 |
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