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    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/106477

    Title: Instagram相片之色彩分析及應用
    Color analysis of Instagram photos and its application
    Authors: 林儀婷
    Lin, Yi-Ting
    Contributors: 廖文宏

    Liao, Wen-Hung
    Lin, Yi-Ting
    Keywords: 社群網絡
    Social network
    Color space analysis
    Machine learning
    Sentiment analysis
    Image and emotion
    Date: 2016
    Issue Date: 2017-02-08 16:51:04 (UTC+8)
    Abstract: 近來Instagram成為流行的分享照片社交平台。在上傳影像到網路社交平台時,人們透過套用不同的濾鏡來表達他們的感受。然而,對於修改過的影像,我們不太可能逆向推回得知影像套用了什麼樣的濾鏡。本研究嘗試透過定義出十種影像風格,對應於一些最常應用的濾鏡,來解決這種逆向工程問題。因此,原始問題被轉化為分類問題,並可以使用機器學習方法來解決。為了生成訓練數據,我們根據用戶投票收集標記的結果。根據我們的實驗,在調查中概述的十個類別中,投票的結果有很高的共識。我們在HSV空間中使用分析出的顏色特徵來區分影像風格,並採用支持向量機(SVM)做分類。驗證我們數據集中的Top 1和Top 3準確度分別為64%和96%,顯示機器分類的效能與人類觀察者的效能相當。最後,我們導入數位著名攝影師的作品,進行個案研究,以測試風格識別和情感分析結果。
    Recently, Instagram has become a very popular social media platform for sharing photos. People apply different type of filters to express their feelings when posting photos on social networking sites. Given a filtered image, it is difficult, if not possible, to determine which filter has been applied to obtain the observed effects. This study attempts to address this reverse engineering problem by defining ten image styles corresponding to some of the most frequently applied filters. As such, the original question is cast into a classification problem which can be solved using machine learning approaches. To generate training data, we collected the labeled results based on user votes. Consensuses among users are found to be high in the ten categories outlined in our investigation. We employ color features in the HSV space to characterize image styles. Support vector machine (SVM) is then used for classification. The accuracies for top-1 and top-3 category using our dataset are 64% and 96%, respectively. The performance of machine classification is comparable to that of human observers. Finally, works by famous photographers are brought in to validate the style recognition and sentiment analysis results.
    Reference: [1] 世界衛生組織統計之憂鬱症人數http://www.who.int/mediacentre/factsheets/fs369/en/
    [2] Gonzalez, Rafael C., and E. Richard. "Woods, digital image processing." ed: Prentice Hall Press, ISBN 0-201-18075-8 (2002).
    [3] Wikipedia contributors, "HSL and HSV," Wikipedia, The Free Encyclopedia, https://en.wikipedia.org/w/index.php?title=HSL_and_HSV&oldid=756001150 (accessed December 21, 2016).
    [4] 色彩心理學:http://td026544.pixnet.net/blog/post/32030867-%E8%89%B2%E5%BD%A9%E5%BF%83%E7%90%86%E5%AD%B8
    [5] 彭姝樺 色彩暗號:關於那些人的顏色學,心理事。尖端出版股份有限公司,2011。
    [6] Reece, Andrew G., and Christopher M. Danforth. "Instagram photos reveal predictive markers of depression." arXiv preprint arXiv:1608.03282 (2016).
    [7] Lee, Honglak, et al. "Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations." Proceedings of the 26th annual international conference on machine learning. ACM, 2009.
    [8] Shin, Laura (June 26, 2012). "Google brain simulator teaches itself to recognize cats". SmartPlanet. Retrieved February 11, 2014.
    [9] Cortes, C.; Vapnik, V. "Support-vector networks". Machine Learning. 1995, 20 (3): 273–297.
    [10] Hsu, Chih-Wei, Chih-Chung Chang, and Chih-Jen Lin. "A practical guide to support vector classification." (2003): 1-16.
    [11] LIBSVM https://www.csie.ntu.edu.tw/~cjlin/libsvm/
    [12] Pang, Bo, and Lillian Lee. "Opinion mining and sentiment analysis." Foundations and trends in information retrieval 2.1-2 (2008): 1-135.
    [13] 彭聲揚 透過圖片標籤觀察情緒字詞與事物概念之關聯,政治大學碩士論文,2011年7月
    [14] 陳育修 藉由孿生網路進行不受濾鏡影響之社群網路圖片分類,台灣大學碩士碩文,2015年7月
    [15] Barrett, Lisa Feldman. "Valence is a basic building block of emotional life." Journal of Research in Personality 40.1 (2006): 35-55.
    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0103971007
    Data Type: thesis
    Appears in Collections:[資訊科學系碩士在職專班] 學位論文

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