[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120202-en":3,"doc-seo-120202-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120202,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Decoding Style - Leveraging Machine Learning for Fashion Trend Detection - Bachelor Thesis","Fashion trend identification faces volatility and significant environmental impact, making more data-driven methods essential. This bachelor thesis explores combining machine learning with fashion analysis to detect recurring style patterns within apparel images. Using image clustering techniques, the study extracts meaningful signals about Spring/Summer 2024 color preferences, item types, and material/print trends, including examples such as red colors, dresses, and stripes. Results are cross-referenced with traditional fashion publications for accuracy assessment, and limitations are discussed alongside future research directions. Ultimately, the approach supports more sustainable, efficient inventory planning by aligning production with consumer preferences.","DECODING STYLE: LEVERAGING MACHINE LEARNING FOR  \nFASHION TREND DETECTION  \nby  \nKORA DUMPERT  \nA THESIS  \nPresented to the Department of Data Science  \nand the Robert D. Clark Honors College in partial fulfillment of the requirements for the degree of  \nBachelor of Science  \nMay 2024  \nAn Abstract of the Thesis of  \nKora Dumpert for the degree of Bachelor of Science  \nin the Department of Data Science to be taken June 2024  \nTitle: Decoding Style: Leveraging Machine Learning for Fashion Trend Detection  \nApproved:  Dr. Thanh Nguyen   \nPrimary Thesis Advisor  \nClothing, once a necessity for human survival, has evolved into a powerful means of selfexpression and social identification. Today, the fashion industry stands as a multi-trillion-dollar global force, shaping economies and cultures. However, its volatile nature and environmental footprint necessitate innovative approaches to trend identification and inventory management. This research explores the fusion of machine learning techniques with fashion trend analysis to offer an accessible solution. By leveraging image clustering algorithms, this study identifies key patterns and trends within fashion apparel. The research unveils significant insights into Spring/Summer 2024 color preferences, item types, and material/print trends. An example of trends identified include red colors, dresses, and stripes. Notably, the identified trends are crossreferenced with traditional fashion publications to assess accuracy. While the study acknowledges certain limitations, particularly in item type differentiation, it proposes avenues for future research. Ultimately, this research not only offers a glimpse into the future of fashion trend analysis but also presents a pathway towards more sustainable and efficient inventory planning. By harnessing the power of machine learning, the fashion industry can align production with consumer preferences, minimizing waste and environmental impact while maximizing economic efficiency.  \nAcknowledgements  \nI would like to express my deepest gratitude to my advisors, Dr. Thanh Nguyen, and Dr. Trond Jacobsen, for their unwavering support and patience throughout the thesis process. Dr. Nguyen, your instrumental role in guiding me from initial discussions to project implementation has been invaluable. Without your expertise and guidance, this thesis would not have come to fruition. Dr. Jacobsen, your support during my time at the Clark Honors College has been truly indispensable. I would also like to extend my thanks to Dr. Stephen Fickas for his valuable input on my methodology and experimental design, which played a crucial role in turning my ideas into reality.  \nTo my family and friends, thank you for being my rock and unwavering support system. To my parents, thank you for instilling in me the value of hard work and for your constant encouragement throughout my life. To my sister, Elise, your light-hearted perspective, and infectious laughter have provided much-needed relief during the challenges ofthis process. Tomy best friend Lauren, your support and guidance in integrating personal elements into this academic endeavor have been invaluable. And to all my friends, thank you for reminding me to strike a balance between academic achievement and savoring the moments of joy during my final year of college.  \nTable of Contents  \nChapter 1: Introduction 9  \nChapter 2: Literature Review 13  \n2.1 Machine Learning for image analysis 13  \n2.2 Types of Machine Learning 13  \n2.3 Current Work 14  \nChapter 3: Methodology 17  \n3.1 Dataset 17  \n3.2 Image Pre-Processing 18  \n3.3 Clustering 22  \n3.3.1 DBSCAN 22  \n3.3.2 OPTICS 23  \n3.3.3 Agglomerative 24  \n3.3.4 K-means 25  \n3.4 Implementation 27  \n3.4.1 DBSCAN 30  \n3.4.2 OPTICS 33  \n3.4.3 Agglomerative 35  \n3.4.4 K-Means 36  \n3.5 Choosing a Clustering Method 37  \n3.6 Identifying Trends 38  \n3.7 Assessing Accuracy 38  \nChapter 4: Experiment 39  \n4.1 K-means Cluster Image Examples 40  \n4.2 Cluster Contents and Image ","cbCaipsm60bswC2i","https://ap.wps.com/l/cbCaipsm60bswC2i","pdf",2788711,1,78,"English","en",105,"# Introduction\n## Motivation and research goals\n# Literature Review\n## Machine Learning for image analysis\n## Types of Machine Learning\n## Current Work\n# Methodology\n## Dataset\n## Image Pre-Processing\n## Clustering (DBSCAN, OPTICS, Agglomerative, K-means)\n## Implementation\n## Choosing a Clustering Method\n## Identifying Trends\n## Assessing Accuracy\n# Experiment\n## K-means Cluster Image Examples\n## Cluster Contents and Image Makeup\n## Results\n## Accuracy & Evaluation\n# Conclusion\n## References\n## Supporting Materials","[{\"question\":\"What problem does the thesis address in fashion trend detection?\",\"answer\":\"It addresses the fashion industry’s volatility and environmental footprint by proposing an ML-based method for more accessible, data-driven trend identification and inventory planning.\"},{\"question\":\"Which machine learning techniques are used to find trends from apparel images?\",\"answer\":\"The study uses image clustering algorithms, including DBSCAN, OPTICS, agglomerative clustering, and K-means, followed by trend identification and accuracy assessment.\"},{\"question\":\"How are the identified fashion trends evaluated for accuracy?\",\"answer\":\"Identified trends are cross-referenced with traditional fashion publications, and the experiments include accuracy and evaluation using clustering results.\"}]","Decoding Style - 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