Fashion Finder : Ai-Powered Image Analysis and Online Shopping Integration App
DOI:
https://doi.org/10.32628/CSEIT2410234Keywords:
Fashion, AI, Imaging, Object DetectionAbstract
The proposed mobile application aims to revolutionize the fashion industry by providing users with a seamless way to discover and purchase clothing items through image recognition technology. The app allows users to either capture images of clothing items through their device's camera or upload images from their stored files. Leveraging machine learning and computer vision algorithms, the app identifies the corresponding clothing items within the images. Upon identification, the app interfaces with e-commerce platforms such as Amazon and Flipkart to search for similar items available for purchase. The development process involves extensive research, design, and implementation of features including image recognition, camera/file upload functionality, API integration with e-commerce platforms, user authentication, and additional user-friendly features. Through rigorous testing, deployment on major app stores, and continuous maintenance, the app aims to offer a seamless and engaging experience for fashion enthusiasts while adhering to user privacy and data security standards.
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Thwe, Y., Jongsawat, N. and Tungkasthan, A., 2023. Accurate fashion and accessories detection for mobile application based on deep learning. International Journal of Electrical & Computer Engineering (2088-8708), 13(4). DOI: https://doi.org/10.11591/ijece.v13i4.pp4347-4356
Martinez, B.M., Smith-Glaviana, D., McAndrews, L. and Manrique, A., 2023. “Eye Shopping” on Fashion Mobile Apps: An Exploratory Study of Design Features. International Journal of Costume and Fashion, 23(1), pp.29-45. DOI: https://doi.org/10.7233/ijcf.2023.23.1.029
Wojciechowski, J., Lisowska, R. and Skrzetuska, E., 2023. State of the art of presentation of clothing textiles in E-commerce with size matching issues. AUTEX Research Journal, 23(4), pp.532-542. DOI: https://doi.org/10.2478/aut-2022-0030
Liu, L., Zhang, H., Zhou, D. and Shi, J., 2023. Toward fashion intelligence in the big data era: state-of-the-art and future prospects. IEEE Transactions on Consumer Electronics. DOI: https://doi.org/10.1109/TCE.2023.3285880
Tang, C., Zakaria, N. and Ruznan, W.S., 2023. The development of anti-fall functional clothing for elderly. Global Health Journal. DOI: https://doi.org/10.1016/j.glohj.2023.10.001
Chang, I.C., Yen, C.E., Chang, H.F., Chen, Y.W., Hsu, M.T., Wang, W.F., Yang, D.Y. and Hsieh, Y.H., 2024. An Integrated YOLOv5 and Hierarchical Human-Weight-First Path Planning Approach for Efficient UAV Searching Systems. Machines, 12(1), p.65. DOI: https://doi.org/10.3390/machines12010065
Maresca, S., 2023. Re-Fashioning Time: An Object-Based Approach to the History of Style. In New Approaches to Decolonizing Fashion History and Period Styles (pp. 7-22). Focal Press. DOI: https://doi.org/10.4324/9781003278184-2
Wang, C., Chen, M., Wang, Q. and Fang, Y., 2023. The study of value network reconstruction and business model innovation driven by entrepreneurial orientation. International Entrepreneurship and Management Journal, 19(4), pp.2013-2036. DOI: https://doi.org/10.1007/s11365-023-00869-y
Islam, M.D., Sultan, M.T., Kabir, A. and Mustarin, S., 2023. Design and Implementation of Workout Buddy Finder App (Doctoral dissertation, Sonargaon University (SU)).
Mumpuni, R., Santoso, B.J., Wahanani, H.E., Rahajoe, A.D., Maulana, H. and Maghfiro, F.N., 2023, October. Implementation of the SMART Method and Location Based Service Technology in a Sewing Service Search Application. In 2023 IEEE 9th Information Technology International Seminar (ITIS) (pp. 1-5). IEEE. DOI: https://doi.org/10.1109/ITIS59651.2023.10420041
Liu, N., Lin, J., Guo, S. and Shi, X., 2023. Fashion platform operations in the sharing economy with digital technologies: recent development and real case studies. Annals of Operations Research, 329(1), pp.1175-1195. DOI: https://doi.org/10.1007/s10479-022-04544-3
Antonucci, A., Bevilacqua, P., Leonardi, S., Paolopoli, L. and Fontanelli, D., 2023. Humans as path-finders for mobile robots using teach-by-showing navigation. Autonomous Robots, 47(8), pp.1255-1273. DOI: https://doi.org/10.1007/s10514-023-10125-5
Mortezaei, S., Rabiei, R., Asadi, F. and Emami, H., 2023. Development and usability evaluation of a mHealth application for albinism self-management. BMC Medical Informatics and Decision Making, 23(1), p.106. DOI: https://doi.org/10.1186/s12911-023-02202-7
Koltermann, K., Jung, W., Blackwell, G., Pinney, A., Chen, M., Cloud, L., Pretzer-Aboff, I. and Zhou, G., 2023, June. FoG-Finder: Real-time Freezing of Gait Detection and Treatment. In Proceedings of the 8th ACM/IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies (pp. 22-33). DOI: https://doi.org/10.1145/3580252.3586980
Aguirre, E. and García-Silvente, M., 2023. Detecting and tracking using 2D laser range finders and deep learning. Neural Computing and Applications, 35(1), pp.415-428. DOI: https://doi.org/10.1007/s00521-022-07765-6
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