Web Browser Add-on for Efficient Image Labeling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Creating training data for deep learning models, such as image recognition, is time-consuming and costly due to the lack of publicly available specific data and the low motivation of the general public to use labeling tools or crowdsourcing services, which require setup and learning.
Innovation Solution
A method that integrates a web browser add-on to display and manage label requests, allowing users to select and assign labels to images during normal browsing, storing the data for later use in a labeling server, thereby reducing the psychological hurdle and providing a motive through cooperative data accumulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional labeling tools are used, then labeling accuracy can be improved, but the time required and complexity increase significantly
Solution Approach 1:
The labeling task is segmented into multiple micro-tasks distributed across different users. Each user performs simple labeling actions on images they encounter during normal browsing, rather than one user performing all labeling tasks sequentially in a dedicated tool. This segmentation enables parallel processing and reduces total labeling time while maintaining accuracy through distributed human judgment.
Solution Approach 2:
The invention merges the labeling function with the general-purpose web browser that users already use for information seeking. By combining these two previously separate functions into a unified system, users can perform labeling tasks incidentally during normal browsing without requiring separate tool setup or dedicated time, thereby reducing labeling time while preserving accuracy through natural human judgment.
2Measurement precision
If dedicated labeling tools are implemented, then labeling precision improves, but device complexity and ease of operation deteriorate
Solution Approach 1:
The web browser is transformed from a single-function information access tool into a multi-functional platform that simultaneously performs web browsing and image labeling. This universality allows the same interface users are already familiar with to serve dual purposes, eliminating the need to learn new tools while maintaining labeling precision through structured workflows embedded within the browser environment.
Solution Approach 2:
Users perform labeling tasks using their existing browsing behavior and knowledge without requiring specialized training or dedicated labeling tool setup. The system leverages users' natural ability to identify and categorize images during normal web exploration, making the complex labeling process as easy as regular browsing while maintaining precision through consistent categorization schemes.
3Productivity
If crowdsourcing services are used, then productivity increases, but cost increases significantly
Solution Approach 1:
Users perform labeling tasks as a voluntary contribution rather than as paid work. By leveraging users' inherent motivation to share information and participate in online communities, the system achieves high labeling throughput without incurring crowdsourcing costs. Users self-select images to label during browsing, eliminating the need for financial incentives while maintaining productivity.
Solution Approach 2:
The system implements feedback mechanisms where users can view their labeling contributions and see how their work helps create training datasets. This feedback loop motivates continued participation and maintains high labeling throughput through intrinsic motivation rather than extrinsic financial rewards, thereby achieving productivity without the associated costs of paid crowdsourcing.
Data Source
AI summary
A computer executes a display procedure of displaying a list of character strings registered in advance in response to a predetermined operation for data forming a part of a web page; and a storage procedure of storing a character string selected by a user from the list into a storage device in association with the data, to thereby achieve more efficient labeling of data.