A Framework and Techniques for Image-based Search Application with an E-commerce Domain
Ishu Bansal, Sakshi Dhar, Yuvraj, Gitanjali, Nikam · 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI) · 2021
Traditionally, most end-users use retrieval systems to write questions or queries and get text results. But end-users continuously expect search engines to be “intelligent” and to be able to manage more than just written queries. End-users want a retrieval system to explore cyberspace using an image, as with the built-in camera on a mobile phone to send relevant resemblance, videos, and other formats. The primary aim of this research work is to address more recent tools and techniques. The study found that convolutional neural networks can use to break down divisions and retrieval issues. Image details are much bigger than text data, and ocular particular cannot be identified by using old techniques, that are designed to identify text details. Therefore, Content-Based Image Retrieval [CBIR] has acquired significant benefits from research society. The paper discusses various CBIR systems that work with specific attributes at a minor plane of an old user input image, making it difficult for users to edit input and provide adequate retrieval results. Additionally, this paper explores the flutter framework with dart language along with the aim to mitigate the fundamental issue that app developers have been facing for so long-maintaining multiple apps for multiple platforms.