Large-scale image analysis using docker sandboxing
This work addresses scalability and security issues in commercial image analysis systems, but it is incremental as it builds on existing technologies like GPUs and Docker.
The paper tackles the challenge of designing scalable software architecture for large-scale image analysis by introducing Cortexica, a framework that leverages GPU scalability and Docker sandboxing, resulting in a flexible and secure system for commercial applications like product search in images.
With the advent of specialized hardware such as Graphics Processing Units (GPUs), large scale image localization, classification and retrieval have seen increased prevalence. Designing scalable software architecture that co-evolves with such specialized hardware is a challenge in the commercial setting. In this paper, we describe one such architecture (\textit{Cortexica}) that leverages scalability of GPUs and sandboxing offered by docker containers. This allows for the flexibility of mixing different computer architectures as well as computational algorithms with the security of a trusted environment. We illustrate the utility of this framework in a commercial setting i.e., searching for multiple products in an image by combining image localisation and retrieval.