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    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/143919
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/143919


    Title: 從圖像分類探討關鍵變數的篩選
    A Study of Key Variable Selection in Image Classification
    Authors: 張和
    Chang, Ho
    Contributors: 余清祥
    陳麗霞

    Yue, Ching-Syang
    Chen, Li Xia

    張和
    Chang, Ho
    Keywords: 圖像辨識
    資料結構化
    圖像風格
    維度縮減
    機器學習
    Date: 2023
    Issue Date: 2023-03-09 18:58:32 (UTC+8)
    Abstract: 隨著電腦發展及科技進步,文字、圖像、語音等非結構化資料的儲存及分析日漸活躍,大數據分析愈來愈普遍應用於生活之中,讓我們能夠體驗數位化時代的方便。以圖像資料為例,車牌辨識、人臉辨識等人工智慧的大量運用,不但讓身份確認更有效率,也減少人力成本及提高辨識準確性,可預期處理圖像資料的需求將更為多元。本文也以圖像辨識為研究目標,探討如何結構化圖像資料,找出較為合適的變數產生方式,作為圖像分類的判斷依據。除了提出圖像資料結構化的方法,本文也考量常見分析圖像的變數,包括:顏色、色調等定義方式,將上述變數代入分類模型,再與統計、機器學習模型比較,找出最佳圖像資料的分析組合。
    除了使用常見的三原色、冷暖色、邊緣、紋理、位置等方法將圖像資料結構化,本文提出將圖像以等分切割(Splitting)成k×k塊,並以百分位數的方式篩選上述結構化資料的主要特徵,代入執行速度相對較快的統計模型(如邏輯斯迴歸),並與機器學習模型比較分類成效。本文考量五種圖像資料,包含臺灣報紙頭版、美國Vogue雜誌封面、台灣Vogue雜誌封面、十九世紀油畫資料(寫實派、印象派)、超寫實油畫,透過交叉驗證比較各種圖像變數定義、分類模型以及維度縮減結果。此外,本文提出的切割法也具有詮釋的功能,可找出區隔圖像風格的重要特徵,也能作為維度縮減的替代方法。研究結果顯示:圖像的等分切割法之分類結果最佳,準確性略高於類神經網路,兩者都明顯優於三原色等常見方法;另外,等分切割法僅需使用大約40%原始資料的變數即可達到原始資料97%變數的效果。
    Reference: 一、中文文獻
    1. 王宇慶、劉維亞、王勇(2008)。「一種基於局部變異和結構相似度的圖像質量評價方法」,《光電子,激光》,第11期,頁1546-1553。
    2. 任大勇、賈振紅、楊傑(2019)。「結合位圖切割和區域合併的彩色圖像分割」,《計算機工程與應用》,55(2),頁162-167。
    3. 安衛超、閻婷、張楠、張彬、相洁、曹銳、王彬(2021)。「病理圖像紋理分析在胃癌MSI預測中的應用研究」,《計算機工程與應用》,57(24),頁205-211。
    4. 李偉斌、馬洪林、易賢、趙凡、李維浩(2019)。「基於色彩空間變換的彩色圖像分割法」,《計算機工程與應用》,55(9),頁162-167。
    5. 胡毅(2015)。「米勒《拾穗》賞析」,《時代文學(下半月)》,第7期,頁75-75。
    6. 郭麗、黃元元、楊靜宇(2004)。「用分塊圖像特徵進行商標圖像檢索」,《計算機輔助設計與圖形學學報》,第7期,頁968-972。
    7. 黃衍翠(2010)。「從《日出·印象》談印象派油畫之美」,《時代文學(上半月)》,第3期,頁229-231。
    8. 楊賢藝(2006)。「論印象派繪畫的藝術特色」,《藝術教育》,第4期,頁94-95。
    9. 廖國紅、齊軍、黃光林(2007)。「基於支持向量機方法的醫學圖像分割」,《計算機工程與應用》,43(29),頁217-219。
    10. 龔如森 (2016)。「西班牙藝術夜空裡的星光-寫實主義的委拉斯蓋茲與浪漫主義的哥雅」,中國文化大學藝術學院美術學系學系碩士論文。
    二、英文文獻
    1. Alara* H., and Fernandezb P. (2021) “Classifying mosquito presence and genera using median and interquartile values from 26-filter wingbeat acoustic properties.”, Procedia Computer Science, 193, 453-463.
    2. Agrawal S., Verma N., Tamrakar P., and Sircar P. (2011) “Content based color image classification using SVM.”, IEEE International Conference on Information Technology, New Generations, 1090 – 1094.
    3. Agarwal S., Karnick H., Pant N., and Patel U. (2015) “Genre and style-based painting classification.”, IEEE Winter Conference on Applications of Computer Vision, 588 – 594.
    4. Barni M., Pelagotti A., and Piva A. (2005) “Image processing for the analysis and conservation of paintings: opportunities and challenges.”, IEEE Signal Process Magazine, 22, 141–144.
    5. Cheng Y. C. and Chen S. Y. (2001) “Image classification using color, texture and regions.”, Image and Vision Computing, 21, 759-776.
    6. Ji M., and Yuyu Y. (2019) “Dimension reduction of image deep feature using PCA.”, Journal of Visual Communication and Image Representation, 63, 102578.
    7. Junhua C. and Jing L. (2012) “Research on color image classification based on HSV color space.”, 2012 Second International Conference on Instrumentation, Measurement, Computer, Communication and Control, 944 – 947.
    8. Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., Liu, T.-Y. (2017) “Lightgbm: A highly efficient gradient boosting decision tree.”, Advances in neural information processing systems 2017, 30.
    9. LOWE D. (2004) “Distinctive image features from scale-invariant key points.”, International Journal of Computer Vision, 60(2), 91–110.
    10. Ogunleye A. and Wang, Q. G. (2019) “XGBoost model for chronic kidney disease diagnosis.”, IEEE ACM Transactions on Computational Biology and Bioinformatics, 17(6), 2131 – 2140.
    11. Robert M., Shanmugam K., and Dinstein I. (1973) “Textural features for image classification.”, IEEE Transactions on Systems, Man and Cybernetics, SMC-3(6), 610 – 621.
    12. Süsstrunk, S., Buckley, R., and Swen, S. (1999) “Standard RGB color spaces.”,
    Color and Imaging Conference, 127–134.
    13. Tian, D. P. (2013) “A review on image feature extraction and representation techniques.”, International Journal of Multimedia and Ubiquitous Engineering, 8, 385–396.
    14. Wang, X. and Nicolas D. (2009) “GLCM textured based fractal method for evaluating fabric surface roughness.”, Canadian Conference on Electrical and Computer Engineering, 104 – 107.
    15. Xin M. and Wang Y. (2019) “Research on image classification model based on deep convolution neural network.”, EURASIP Journal on Image and Video Processing, 40.
    Description: 碩士
    國立政治大學
    統計學系
    109354028
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0109354028
    Data Type: thesis
    Appears in Collections:[統計學系] 學位論文

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