Robust image classification system via cloud computing, aligned multimodal embeddings, centroids and neighbours

• Main Authors: KOH, Wei Lun, KOH, Boon Yong, DAI, Bing Tian
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Image classification; Cloud; AWS; Application; Crowd-sourced data; Databases and Information Systems; Graphics and Human Computer Interfaces; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/9619; https://ink.library.smu.edu.sg/context/sis_research/article/10619/viewcontent/1_s2.0_S2666827024000598_pvoa_cc_nc_nd.pdf
• Institution: Singapore Management University

We propose a framework for a cloud-based application of an image classification system that is highly accessible, maintains data confidentiality, and robust to incorrect training labels. The end-to-end system is implemented using Amazon Web Services (AWS), with a detailed guide provided for replication, enhancing the ways which researchers can collaborate with a community of users for mutual benefits. A front-end web application allows users across the world to securely log in, contribute labelled training images conveniently via a drag-and-drop approach, and use that same application to query an up-to-date model that has knowledge of images from the community of users. This resulting system demonstrates that theory can be effectively interlaced with practice, with various considerations addressed by our architecture. Users will have access to an image classification model that can be updated and automatically deployed within minutes, gaining benefits from and at the same time providing benefits to the community of users. At the same time, researchers, who will act as administrators, will be able to conveniently and securely engage a large number of users with their respective machine learning models and build up a labelled database over time, paying only variable costs that is proportional to utilization.