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題名 以廣告影像感知特徵建立智慧型分類
Established intelligent classification based on the feature of advertising image作者 古雅琪
Ku, Ya-Chi貢獻者 羅崇銘
Lo, Chung-Ming
古雅琪
Ku, Ya-Chi關鍵詞 電子商務
商品形象
色彩構成
卷積神經網路
影像分類
E-commerce
Product image
Color extraction
Convolutional neural network
Image classification日期 2022 上傳時間 2-九月-2022 14:58:58 (UTC+8) 摘要 受到時代演進、資訊技術提升以及環境改變的影響，電子商務的蓬勃發展 已成為趨勢。電子商務販售的商品眾多，因此其最大的考驗莫過於商品歸類， 而透過後設資料的描述能使商品歸類更有效率。目前電子商務商品的後設資料 多透過手動輸入及分類，需耗費大量的人力資源，若能夠以自動化分類商品便 能夠減輕成本及人力資源的負擔。本研究為建立高適用性的自動化商品分類系 統，以聯合國標準商品與服務編碼分類系統作為依據，將 momo 購物網作為研 究目標，擷取 9 大類商品影像資料集後，根據影像所傳達出商品形象的色彩構 成與卷積神經網路兩種方法建立自動化分類系統，以解決在商品多樣化的電子 商務下，人工分類的負擔與可能的失誤。結果顯示，雖然企業會根據消費者認 知設計商品的色彩，但利用色彩構成分類 9 類商品圖的最高準確率為 46.50%; 利用卷積神經網路可以達到最高準確率為 83.11%，兩者相較之下利用卷積神經 網路能夠更好地建立自動化商品分類，可以做為電子商務平台上自動化商品分 類的系統技術，讓商品歸類更有效率。
Over recent years, e-commerce become more and more popular with the development of information technology and the environment. E-commerce offers numerous products, and its biggest challenge is how to classify product categories precisely and efficiently with metadata. Currently e-commerce lunches and classify metadata manually, which consumes lots of labor cost, classifying products automatically can decrease both cost and mistakes of manual classification. In this paper, we narrowed the dataset down to 9 categories, by using UNSPSC classification systems as standard to classify products from “momo”, hoping that we can automatically classify categories based on the color extraction and convolutional neural network from product image. Although enterprises will design the color of products according to customer’s perceptions, our results indicate that the accuracy rate of convolutional neural network is 83.11% higher than color extraction 46.50%, which means using convolutional neural network can establish intelligent classification better.參考文獻 未來流通研究所(2020)。【商業數據圖解】2020 台灣「零售&電商」產業市佔 率英雄榜。檢自: https://www.mirai.com.tw/2020-taiwan-retail-ec-market- share-analysis/富邦媒體科技股份有限公司 (2021)。 關於我們。檢自: https://www.fmt.com.tw/about/aboutmomo/經濟部統計處 (2021)。 「宅經濟」發酵，帶動網路銷售額成長。 檢自: https://www.moea.gov.tw/Mns/dos/bulletin/Bulletin.aspx?kind=9&html=1&m enu_id=18808&bull_id=7590Alrumiah, S. S., & Hadwan, M. (2021). Implementing big data analytics in e- commerce: Vendor and customer view. IEEE Access, 9, 37281-37286. doi:10.1109/ACCESS.2021.3063615Amazon. (2021). Amazon. Retrieved from https://www.amazon.com/- /zh_TW/ref=nav_logo?currency=TWD&language=en_USBelgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS journal of photogrammetry and Remote Sensing, 114, 24-31. doi:10.1016/j.isprsjprs.2016.01.011Bergamaschi, S., Guerra, F., & Vincini, M. (2002). Product classification integration for e-commerce. Proceedings. 13th International Workshop on Database and Expert Systems Applications, doi:10.1109/DEXA.2002.1046004Biers, K., & Richards, L. (2005). Color as a factor of product choice in e-commerce. Review of Business Information Systems (RBIS), 9(4), 33-40.Bora, D. J., Gupta, A. K., & Khan, F. A. (2015). Comparing the Performance of L* A* B* and HSV color spaces with respect to color image segmentation. arXiv preprint arXiv:1506.01472.Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32.Bureau, U. S. C. (2021). North American Industry Classification System. Retrieved from https://www.census.gov/naics/?99967Burney, S. A., & Tariq, H. (2014). K-means cluster analysis for image segmentation. International Journal of Computer Applications, 96(4).Casas, M. C., & Chinoperekweyi, J. (2019). Color psychology and its influence on consumer buying behavior: A case of apparel products. Saudi Journal of Business and Management Studies, 4(5), 441-456.Castelvecchi, D. (2016). Can we open the black box of AI? Nature News, 538(7623), 20.Garaus, M., & Halkias, G. (2020). One color fits all: product category color norms and (a)typical package colors. Review of managerial science, 14(5), 1077- 1099.Goswami, A., Chittar, N., & Sung, C. H. (2011). A study on the impact of product images on user clicks for online shopping. Proceedings of the 20th international conference companion on World wide web, 45-46.He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition, 770-778.Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. Proceedings of the IEEE conference on computer vision and pattern recognition, 4700-4708.Hwangbo, H., Kim, Y. S., & Cha, K. J. (2018). Recommendation system development for fashion retail e-commerce. Electronic Commerce Research and Applications, 28, 94-101. doi: 10.1016/j.elerap.2018.01.012Jha, B. K., S. G, G., & V. K, R. (2021). E-Commerce Product Image Classification using Transfer Learning. 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), 904-912.Kale, A., & Mente, R. (2018). M-Commerce: Services and applications. Int. J. Adv. Sci. Res, 3(1), 19-21.Khanuja, R. K. (2019). Optimizing E-Commerce Product Classification Using Transfer Learning.Kreitler, H., & Kreitler, S. (1972). Psychology of the arts. Duke University Press,14.Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 1097-1105.Labrecque, L. I., & Milne, G. R. (2012). Exciting red and competent blue: the importance of color in marketing. Journal of the Academy of Marketing Science, 40(5), 711-727. doi:10.1007/s11747-010-0245-yLane, R. (1991). Does orange mean cheap? Forbes, 148(14), 144.Lauren, I. L., & George, R. M. (2013). To be or not to be different: Exploration of norms and benefits of color differentiation in the marketplace. Marketing letters, 24(2), 165-176. doi:10.1007/s11002-012-9210-5LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.Li, G., & Li, N. (2019). Customs classification for cross-border e-commerce based on text-image adaptive convolutional neural network. Electronic Commerce Research, 19(4), 779-800. doi:10.1007/s10660-019-09334-xLiu, T., Wang, R., Chen, J., Han, S., & Yang, J. (2018). Fine-grained classification of product images based on convolutional neural networks. Advances in 53 Molecular Imaging, 8(04), 69.MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Berkeley symposium on mathematical statistics and probability, 281-297.Maier, E., & Dost, F. (2018). The positive effect of contextual image backgrounds on fluency and liking. Journal of Retailing and Consumer Services, 40, 109-116. doi:10.1016/j.jretconser.2017.09.003Majid, E. S. A., Kamaruddin, N., & Mansor, Z. (2015, 10-11 Aug. 2015). Adaptation of usability principles in responsive web design technique for e-commerce development. 2015 International Conference on Electrical Engineering and Informatics (ICEEI), 726-729.McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D., & Barton, D. (2012). Big data: the management revolution. Harvard business review, 90(10), 60-68.Morton, J. (2021). Why Color Matters. Retrieved from https://www.colorcom.com/research/why-color-mattersNakwaski, M., & Zabierowski, W. (2010, 23-27 Feb. 2010). Content management system for web portal. 2010 International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science (TCSET), 233-235.Narkhede, P. R., & Gokhale, A. V. (2015). Color image segmentation using edge detection and seeded region growing approach for CIELab and HSV color spaces. 2015 International Conference on Industrial Instrumentation and Control (ICIC), 1214-1218.Oyewole, S., & Olugbara, O. (2018). Product image classification using Eigen Colour feature with ensemble machine learning. Egyptian Informatics Journal, 19(2), 83-100. doi: 10.1016/j.eij.2017.10.002Pantelimon, F.-V., Georgescu, T. M., & Posedaru, B. Ş. (2020). The impact of mobile e-commerce on gdp: A comparative analysis between romania and germany and how covid-19 influences the e-commerce activity worldwide. Informatica Economica, 24(2), 27-41. doi:10.24818/issn14531305/24.2.2020.03Priluck Grossman, R., & Wisenblit, J. Z. (1999). What we know about consumers` color choices. Journal of marketing practice : Applied marketing science, 5(3), 78-88. doi:10.1108/EUM0000000004565Refaeilzadeh, P., Tang, L., & Liu, H. (2016). Cross-Validation. In L. Liu & M. T. Özsu (Eds.), Encyclopedia of Database Systems, 1-7. Springer New York. doi:10.1007/978-1-4899-7993-3_565-2Rothen, N., Seth, A. K., Witzel, C., & Ward, J. (2013). Diagnosing synaesthesia with online colour pickers: maximising sensitivity and specificity. Journal of neuroscience methods, 215(1), 156-160. doi:10.1016/j.jneumeth.2013.02.009Salih, A. A., Zeebaree, S., Abdulraheem, A. S., Zebari, R. R., Sadeeq, M., & Ahmed, O. M. (2020). Evolution of mobile wireless communication to 5G revolution. Technology Reports of Kansai University, 62(5), 2139-2151.Schulten, E., Akkermans, H., Botquin, G., Dörr, M., Guarino, N., Lopes, N., & Sadeh, N. (2001). The e-commerce product classification challenge. IEEE Intelligent systems, 16(4), 86-89.ScrapeHero. (2021). How Many Products Does Amazon Sell? Retrieved from : https://www.scrapehero.com/how-many-products-does-amazon-sell-march- 2021/Singh, M. (2002). E‐services and their role in B2C e‐commerce. Managing Service Quality: An International Journal, 12(6), 434-446. doi:10.1108/09604520210451911Singh, S. R., & Kohli, S. (2015). Enhanced CBIR using color moments, HSV histogram, color auto correlogram, and gabor texture. International Journal of Computer Systems, 2(5), 161-165.Statista. (2021a). Retail e-commerce sales worldwide from 2014 to 2024. Retrived from https://www.statista.com/statistics/379046/worldwide-retail-e-commerce sales/#statisticContainerStatista. (2021b). Retail m-commerce sales via smartphone in the United States from 2018 to 2024. Retrived from https://www.statista.com/statistics/276636/smartphones-us-retail-m- commerce-sales/Stricker, M. A., & Orengo, M. (1995). Similarity of color images. Storage and retrieval for image and video databases III (SPiE), 2420, 381-392.Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition, 1-9.Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. Proceedings of the IEEE conference on computer vision and pattern recognition, 2818-2826.Trent, L. (1993). Color can affect success of products. Marketing news, 27(4). UNSPSC. (2021). Retrieved from https://www.unspsc.org/You, W.T., Sun, L.Y., Yang, Z.Y., & Yang, C.Y. (2019). Automatic advertising image color design incorporating a visual color analyzer. Journal of Computer Languages, 55, 100910. doi: 10.1016/j.cola.2019.100910Zahavy, T., Magnani, A., Krishnan, A., & Mannor, S. (2016). Is a picture worth a thousand words? a deep multi-modal fusion architecture for product classification in e-commerce. arXiv preprint arXiv:1611.09534. 描述 碩士
國立政治大學
圖書資訊與檔案學研究所
109155006資料來源 http://thesis.lib.nccu.edu.tw/record/#G0109155006 資料類型 thesis dc.contributor.advisor 羅崇銘 zh_TW dc.contributor.advisor Lo, Chung-Ming en_US dc.contributor.author (作者) 古雅琪 zh_TW dc.contributor.author (作者) Ku, Ya-Chi en_US dc.creator (作者) 古雅琪 zh_TW dc.creator (作者) Ku, Ya-Chi en_US dc.date (日期) 2022 en_US dc.date.accessioned 2-九月-2022 14:58:58 (UTC+8) - dc.date.available 2-九月-2022 14:58:58 (UTC+8) - dc.date.issued (上傳時間) 2-九月-2022 14:58:58 (UTC+8) - dc.identifier (其他 識別碼) G0109155006 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/141612 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 圖書資訊與檔案學研究所 zh_TW dc.description (描述) 109155006 zh_TW dc.description.abstract (摘要) 受到時代演進、資訊技術提升以及環境改變的影響，電子商務的蓬勃發展 已成為趨勢。電子商務販售的商品眾多，因此其最大的考驗莫過於商品歸類， 而透過後設資料的描述能使商品歸類更有效率。目前電子商務商品的後設資料 多透過手動輸入及分類，需耗費大量的人力資源，若能夠以自動化分類商品便 能夠減輕成本及人力資源的負擔。本研究為建立高適用性的自動化商品分類系 統，以聯合國標準商品與服務編碼分類系統作為依據，將 momo 購物網作為研 究目標，擷取 9 大類商品影像資料集後，根據影像所傳達出商品形象的色彩構 成與卷積神經網路兩種方法建立自動化分類系統，以解決在商品多樣化的電子 商務下，人工分類的負擔與可能的失誤。結果顯示，雖然企業會根據消費者認 知設計商品的色彩，但利用色彩構成分類 9 類商品圖的最高準確率為 46.50%; 利用卷積神經網路可以達到最高準確率為 83.11%，兩者相較之下利用卷積神經 網路能夠更好地建立自動化商品分類，可以做為電子商務平台上自動化商品分 類的系統技術，讓商品歸類更有效率。 zh_TW dc.description.abstract (摘要) Over recent years, e-commerce become more and more popular with the development of information technology and the environment. E-commerce offers numerous products, and its biggest challenge is how to classify product categories precisely and efficiently with metadata. Currently e-commerce lunches and classify metadata manually, which consumes lots of labor cost, classifying products automatically can decrease both cost and mistakes of manual classification. In this paper, we narrowed the dataset down to 9 categories, by using UNSPSC classification systems as standard to classify products from “momo”, hoping that we can automatically classify categories based on the color extraction and convolutional neural network from product image. Although enterprises will design the color of products according to customer’s perceptions, our results indicate that the accuracy rate of convolutional neural network is 83.11% higher than color extraction 46.50%, which means using convolutional neural network can establish intelligent classification better. en_US dc.description.tableofcontents 摘要 iAbstract ii目次 iii表次 v圖次 vi第一章 緒論 1第一節 電子商務 1第二節 商品分類的重要性與挑戰 2第三節 智慧型商品影像分類 7第二章 文獻探討 10第一節 色彩與商品分類 10第二節 商品影像特徵與分類 10第三章 研究方法與步驟 13第一節 利用 UNSPSC 分類碼將商品分類 14第二節 商品影像收集 29第三節 商品影像特徵擷取 30壹 色彩構成分析 30一 色彩分群 30二 色彩矩 34第四節 模型訓練 36壹 機器學習 36一 隨機森林 36二 十折交叉驗證 36貳 深度學習 36一 卷積神經網路 36二 AlexNet 37三 Inception V3 38四 ResNet 152 39五 DenseNet 201 39第四章 實驗結果 40第五章 討論與結論 47第六章 未來方向 51參考文獻 52附錄、卷積神經網路實驗數據 57 zh_TW dc.format.extent 5029034 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0109155006 en_US dc.subject (關鍵詞) 電子商務 zh_TW dc.subject (關鍵詞) 商品形象 zh_TW dc.subject (關鍵詞) 色彩構成 zh_TW dc.subject (關鍵詞) 卷積神經網路 zh_TW dc.subject (關鍵詞) 影像分類 zh_TW dc.subject (關鍵詞) E-commerce en_US dc.subject (關鍵詞) Product image en_US dc.subject (關鍵詞) Color extraction en_US dc.subject (關鍵詞) Convolutional neural network en_US dc.subject (關鍵詞) Image classification en_US dc.title (題名) 以廣告影像感知特徵建立智慧型分類 zh_TW dc.title (題名) Established intelligent classification based on the feature of advertising image en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 未來流通研究所(2020)。【商業數據圖解】2020 台灣「零售&電商」產業市佔 率英雄榜。檢自: https://www.mirai.com.tw/2020-taiwan-retail-ec-market- share-analysis/富邦媒體科技股份有限公司 (2021)。 關於我們。檢自: https://www.fmt.com.tw/about/aboutmomo/經濟部統計處 (2021)。 「宅經濟」發酵，帶動網路銷售額成長。 檢自: https://www.moea.gov.tw/Mns/dos/bulletin/Bulletin.aspx?kind=9&html=1&m enu_id=18808&bull_id=7590Alrumiah, S. S., & Hadwan, M. (2021). Implementing big data analytics in e- commerce: Vendor and customer view. IEEE Access, 9, 37281-37286. doi:10.1109/ACCESS.2021.3063615Amazon. (2021). Amazon. Retrieved from https://www.amazon.com/- /zh_TW/ref=nav_logo?currency=TWD&language=en_USBelgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS journal of photogrammetry and Remote Sensing, 114, 24-31. doi:10.1016/j.isprsjprs.2016.01.011Bergamaschi, S., Guerra, F., & Vincini, M. (2002). Product classification integration for e-commerce. Proceedings. 13th International Workshop on Database and Expert Systems Applications, doi:10.1109/DEXA.2002.1046004Biers, K., & Richards, L. (2005). Color as a factor of product choice in e-commerce. Review of Business Information Systems (RBIS), 9(4), 33-40.Bora, D. J., Gupta, A. K., & Khan, F. A. (2015). Comparing the Performance of L* A* B* and HSV color spaces with respect to color image segmentation. arXiv preprint arXiv:1506.01472.Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32.Bureau, U. S. C. (2021). North American Industry Classification System. Retrieved from https://www.census.gov/naics/?99967Burney, S. A., & Tariq, H. (2014). K-means cluster analysis for image segmentation. International Journal of Computer Applications, 96(4).Casas, M. C., & Chinoperekweyi, J. (2019). Color psychology and its influence on consumer buying behavior: A case of apparel products. Saudi Journal of Business and Management Studies, 4(5), 441-456.Castelvecchi, D. (2016). Can we open the black box of AI? Nature News, 538(7623), 20.Garaus, M., & Halkias, G. (2020). One color fits all: product category color norms and (a)typical package colors. Review of managerial science, 14(5), 1077- 1099.Goswami, A., Chittar, N., & Sung, C. H. (2011). A study on the impact of product images on user clicks for online shopping. Proceedings of the 20th international conference companion on World wide web, 45-46.He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition, 770-778.Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. Proceedings of the IEEE conference on computer vision and pattern recognition, 4700-4708.Hwangbo, H., Kim, Y. S., & Cha, K. J. (2018). Recommendation system development for fashion retail e-commerce. Electronic Commerce Research and Applications, 28, 94-101. doi: 10.1016/j.elerap.2018.01.012Jha, B. K., S. G, G., & V. K, R. (2021). E-Commerce Product Image Classification using Transfer Learning. 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), 904-912.Kale, A., & Mente, R. (2018). M-Commerce: Services and applications. Int. J. Adv. Sci. Res, 3(1), 19-21.Khanuja, R. K. (2019). Optimizing E-Commerce Product Classification Using Transfer Learning.Kreitler, H., & Kreitler, S. (1972). Psychology of the arts. Duke University Press,14.Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 1097-1105.Labrecque, L. I., & Milne, G. R. (2012). Exciting red and competent blue: the importance of color in marketing. Journal of the Academy of Marketing Science, 40(5), 711-727. doi:10.1007/s11747-010-0245-yLane, R. (1991). Does orange mean cheap? Forbes, 148(14), 144.Lauren, I. L., & George, R. M. (2013). To be or not to be different: Exploration of norms and benefits of color differentiation in the marketplace. Marketing letters, 24(2), 165-176. doi:10.1007/s11002-012-9210-5LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). 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