臺灣博碩士論文加值系統

社群媒體的興起帶動了大量社群媒體影響者的出現，而且人人都可以成為社群媒體影響者。該如何吸引觀眾目光及共鳴是許多社群經營者所關心的議題。哪種類型的圖像才能達到良好的互動率、圖像中應該包含哪些元素，也是學術界及業界時常熱論的話題，惟至今尚未有一套科學系統可以一次解答這些問題。基於上述觀察，本研究設計一套智慧圖像建議系統。在提取出圖像的特徵之後，對照片進行互動率好壞的預測，並給予相關修改建議及範例照片供使用者參考。這個系統是基於深度學習與影像處理技術所建構而成。此外，本研究以Instagram上義式披薩產業的圖像作為個案，並定義四種圖像吸引力類型來建構系統。研究結果顯示，系統預測的準確率達到72.90%。在應用上，本研究所提出的智慧圖像建議平台可以提供圖像修改建議，幫助上傳者在上傳照片之前先對照片做預先的修改及評估。

The rise of social media has led to the emergence of a large number of social media influencers, and everyone can become one of them. How to attract the audience's attention and resonance must be the concern of many social media managers. Which type of image can achieve a good engagement rate and what elements should be included in the image are also hot topics in academia and industry. But so far, there is no scientific system that can answer these questions all at once. Based on the above, this study designed an intelligence image advice system. After extracting the features of the image, it can predict the engagement rate of the image and give relevant modification suggestions and sample images for users' reference. The system is constructed based on deep learning and image processing techniques. In addition, this study uses the images of the Italian pizza industry on Instagram as a case study and defines four types of image appeal to construct the system. The results of the study showed that the accuracy of the system prediction reached 72.90%. In application, the intelligence image advice system proposed in this study can provide image modification suggestions and help uploaders make pre-modification and evaluation of images before uploading them.

Abstract i
摘要 ii
Table of content iii
List of Figures iv
List of Table v
Chapter 1. Introduction 1
Chapter 2. Literature review 3
2.1 Social media influencer marketing 3
2.2 Image aesthetics and key features 3
2.3 Quality image classifier 5
Chapter 3. Method 7
3.1 Proposed framework 7
3.2 Data collection 8
3.2.1 Web crawler module 8
3.2.2 Second-hand data collection 9
3.3 Feature extraction 9
3.3.1 Image signal processing features 10
3.3.2 Panoptic segmentation features 11
3.3.3 Face-gender detection features 13
3.3.4 Image composition feature 13
3.3.5 Expert advice features 15
3.4 Calculation and grading of image appeal 19
3.5 Feature reduction 21
3.6 Quality image classifier 22
3.7 Proposed intelligence image advice system 22
Chapter 4. Experiment and discussion 25
4.1 Experiment setup 25
4.2 Construction and evaluation of social media image appeal classifier 25
4.3 Implement of intelligence image advice system 28
Chapter 5. Conclusions 37
References 39
Appendix 1. Feature types and value ranges of the OSF features 43
Appendix 2. CART Model 45

Adagbasa, E. G., Adelabu, S. A., & Okello, T. W. (2022). Application of deep learning with stratified K-fold for vegetation species discrimation in a protected mountainous region using Sentinel-2 image. Geocarto International, 37(1), 142-162.
Ali, J., Khan, R., Ahmad, N., & Maqsood, I. (2012). Random forests and decision trees. International Journal of Computer Science Issues (IJCSI), 9(5), 272.
Arora, A., Bansal, S., Kandpal, C., Aswani, R., & Dwivedi, Y. (2019). Measuring social media influencer index-insights from Facebook, Twitter and Instagram. Journal of Retailing and Consumer Services, 49, 86-101.
Auxier, B., & Anderson, M. (2021). Social media use in 2021. Pew Research Center, 1, 1-4.
Bharati, M. H., Liu, J. J., & MacGregor, J. F. (2004). Image texture analysis: methods and comparisons. Chemometrics and intelligent laboratory systems, 72(1), 57-71.
Bharti, K. K., & Singh, P. K. (2015). Hybrid dimension reduction by integrating feature selection with feature extraction method for text clustering. Expert Systems with Applications, 42(6), 3105-3114.
Caesar, H., Uijlings, J., & Ferrari, V. (2018). Coco-stuff: Thing and stuff classes in context. Proceedings of the IEEE conference on computer vision and pattern recognition,
Chen, W., Shi, Y. Q., & Xuan, G. (2007). Identifying computer graphics using HSV color model and statistical moments of characteristic functions. 2007 ieee international conference on multimedia and expo,
Daniya, T., Geetha, M., & Kumar, K. S. (2020). Classification and regression trees with Gini index. Advances in Mathematics: Scientific Journal, 9(10), 8237-8247.
Fang, Q., Sang, J., Xu, C., & Rui, Y. (2014). Topic-sensitive influencer mining in interest-based social media networks via hypergraph learning. IEEE Transactions on Multimedia, 16(3), 796-812.
Filieri, R., Yen, D. A., & Yu, Q. (2021). # ILoveLondon: An exploration of the declaration of love towards a destination on Instagram. Tourism Management, 85, 104291.
Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., & Garcia-Rodriguez, J. (2017). A review on deep learning techniques applied to semantic segmentation. arXiv preprint arXiv:1704.06857.
Gatys, L. A., Ecker, A. S., & Bethge, M. (2017). Texture and art with deep neural networks. Current opinion in neurobiology, 46, 178-186.
Glucksman, M. (2017). The rise of social media influencer marketing on lifestyle branding: A case study of Lucie Fink. Elon Journal of undergraduate research in communications, 8(2), 77-87.
Greenwood, S., Perrin, A., & Duggan, M. (2016). Social media update 2016. Pew Research Center, 11(2), 1-18.
Hafiz, A. M., & Bhat, G. M. (2020). A survey on instance segmentation: state of the art. International journal of multimedia information retrieval, 9(3), 171-189.
Hollebeek, L. D., Glynn, M. S., & Brodie, R. J. (2014). Consumer brand engagement in social media: Conceptualization, scale development and validation. Journal of interactive marketing, 28(2), 149-165.
Jović, A., Brkić, K., & Bogunović, N. (2015). A review of feature selection methods with applications. 2015 38th international convention on information and communication technology, electronics and microelectronics (MIPRO),
Kay, S., Mulcahy, R., & Parkinson, J. (2020). When less is more: the impact of macro and micro social media influencers’ disclosure. Journal of Marketing Management, 36(3-4), 248-278.
Ki, C.-W. C., Cuevas, L. M., Chong, S. M., & Lim, H. (2020). Influencer marketing: Social media influencers as human brands attaching to followers and yielding positive marketing results by fulfilling needs. Journal of Retailing and Consumer Services, 55, 102133.
Ki, C. W. C., & Kim, Y. K. (2019). The mechanism by which social media influencers persuade consumers: The role of consumers’ desire to mimic. Psychology & Marketing, 36(10), 905-922.
Kim, D., Woo, S., Lee, J.-Y., & Kweon, I. S. (2020). Video panoptic segmentation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,
Kirillov, A., He, K., Girshick, R., Rother, C., & Dollár, P. (2019). Panoptic segmentation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition,
Li, H., & Ngan, K. N. (2011). Learning to extract focused objects from low dof images. IEEE transactions on circuits and systems for video technology, 21(11), 1571-1580.
Li, X., & Chen, D. (2022). A survey on deep learning-based panoptic segmentation. Digital Signal Processing, 120, 103283.
Li, Y., & Xie, Y. (2020). Is a picture worth a thousand words? An empirical study of image content and social media engagement. Journal of Marketing Research, 57(1), 1-19.
Lim, X. J., Radzol, A., Cheah, J., & Wong, M. W. (2017). The impact of social media influencers on purchase intention and the mediation effect of customer attitude. Asian Journal of Business Research, 7(2), 19-36.
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014). Microsoft coco: Common objects in context. European conference on computer vision,
Liu, D., Puri, R., Kamath, N., & Bhattacharya, S. (2020). Composition-aware image aesthetics assessment. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision,
Livens, S., Scheunders, P., Van de Wouwer, G., & Van Dyck, D. (1997). Wavelets for texture analysis, an overview.
Lo, K.-Y., Liu, K.-H., & Chen, C.-S. (2012). Assessment of photo aesthetics with efficiency. Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012),
Lou, C., & Yuan, S. (2019). Influencer marketing: how message value and credibility affect consumer trust of branded content on social media. Journal of Interactive Advertising, 19(1), 58-73.
Lu, X., Lin, Z., Jin, H., Yang, J., & Wang, J. Z. (2015). Rating image aesthetics using deep learning. IEEE Transactions on Multimedia, 17(11), 2021-2034.
Luo, W., Wang, X., & Tang, X. (2011). Content-based photo quality assessment. 2011 international conference on computer vision,
Mahesh, B. (2020). Machine learning algorithms-a review. International Journal of Science and Research (IJSR).[Internet], 9, 381-386.
Mai, L., Le, H., Niu, Y., & Liu, F. (2011). Rule of thirds detection from photograph. 2011 IEEE international symposium on Multimedia,
Masuda, H., Han, S. H., & Lee, J. (2022). Impacts of influencer attributes on purchase intentions in social media influencer marketing: Mediating roles of characterizations. Technological Forecasting and Social Change, 174, 121246.
Matz, S. C., Segalin, C., Stillwell, D., Müller, S. R., & Bos, M. W. (2019). Predicting the personal appeal of marketing images using computational methods. Journal of Consumer Psychology, 29(3), 370-390.
Mohanaiah, P., Sathyanarayana, P., & GuruKumar, L. (2013). Image texture feature extraction using GLCM approach. International journal of scientific and research publications, 3(5), 1-5.
Mou, J. B. (2020). Study on social media marketing campaign strategy--TikTok and Instagram Massachusetts Institute of Technology].
Nouri, M. (2018). The power of influence: traditional celebrity vs social media influencer.
O'Donovan, P., Agarwala, A., & Hertzmann, A. (2011). Color compatibility from large datasets. In ACM SIGGRAPH 2011 papers (pp. 1-12).
Obar, J. A., & Wildman, S. S. (2015). Social media definition and the governance challenge-an introduction to the special issue. Obar, JA and Wildman, S.(2015). Social media definition and the governance challenge: An introduction to the special issue. Telecommunications policy, 39(9), 745-750.
Obrador, P., Schmidt-Hackenberg, L., & Oliver, N. (2010). The role of image composition in image aesthetics. 2010 IEEE International Conference on Image Processing,
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., & Dubourg, V. (2011). Scikit-learn: Machine learning in Python. the Journal of machine Learning research, 12, 2825-2830.
Peng, W., Chen, J., & Zhou, H. (2009). An implementation of ID3-decision tree learning algorithm. From web. arch. usyd. edu. au/wpeng/DecisionTree2. pdf Retrieved date: May, 13.
Pilgrim, K., & Bohnet-Joschko, S. (2019). Selling health and happiness how influencers communicate on Instagram about dieting and exercise: Mixed methods research. BMC public health, 19(1), 1-9.
Priyanka, & Kumar, D. (2020). Decision tree classifier: A detailed survey. International Journal of Information and Decision Sciences, 12(3), 246-269.
Rao, T., Li, X., & Xu, M. (2020). Learning multi-level deep representations for image emotion classification. Neural processing letters, 51(3), 2043-2061.
Singh, R., Shetty, S., Patil, G., & Bide, P. J. (2021). Helmet Detection Using Detectron2 and EfficientDet. 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT),
Song, Y.-Y., & Ying, L. (2015). Decision tree methods: applications for classification and prediction. Shanghai archives of psychiatry, 27(2), 130.
Xue, B., Zhang, M., Browne, W. N., & Yao, X. (2015). A survey on evolutionary computation approaches to feature selection. IEEE Transactions on Evolutionary Computation, 20(4), 606-626.
Yadav, S. K., & Pal, S. (2012). Data mining: A prediction for performance improvement of engineering students using classification. arXiv preprint arXiv:1203.3832.
Zhu, L., & Lerman, K. (2016). Attention inequality in social media. arXiv preprint arXiv:1601.07200.