Image Annotation via Label Extraction and Verification
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Solution Overview
Problem
Existing image annotation techniques suffer from inaccuracies and delays due to third-party tagging by unfamiliar individuals and incorrect links, and they often compromise the visual aesthetics of images by overlaying graphics, failing to maintain the end-user experience.
Innovation Solution
A method and system for electronically annotating images by allowing users to apply labels to specific items within an image, enabling accurate and timely annotation with minimal user effort, while maintaining the image's visual integrity through contextual information and administrator verification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If third parties tag images with product information, then image annotation coverage increases, but annotation accuracy decreases due to lack of product knowledge
Solution Approach 1:
The patent introduces an intermediary verification system where uploaded images are processed through automated recognition algorithms first, then reviewed by human moderators who verify both the image content and the associated tags. This intermediary process ensures that only accurate annotations are published, resolving the contradiction between broad coverage and high accuracy.
Solution Approach 2:
The system implements feedback mechanisms where annotation accuracy is continuously monitored and used to improve the automated recognition algorithms. User corrections and moderator feedback create a learning loop that enhances annotation quality over time while maintaining high productivity through automated initial processing.
2Adaptability or versatility
If third parties tag images after image capture, then annotation diversity increases, but annotation timeliness decreases due to delays
Solution Approach 1:
The system performs preliminary automated tag generation at the time of image upload, rather than waiting for third-party annotation. This preliminary action provides immediate annotation coverage while still allowing for subsequent human review and correction, thus maintaining both timeliness and diversity.
Solution Approach 2:
The annotation process continues continuously through multiple stages: automated recognition generates initial tags immediately, then human moderators refine them, and the system learns from feedback. This continuous process maintains annotation diversity while minimizing time loss through parallel processing stages.
3Loss of information
If overlays are added to images to indicate tagging, then user awareness of annotations increases, but visual aesthetics deteriorate
Solution Approach 1:
The patent extracts the annotation indication function from visual overlays and implements it through data structures and metadata instead. Tags are stored separately from the image file, allowing the image to display without aesthetic degradation while still providing full annotation functionality through hover effects, side panels, or other non-intrusive interfaces.
Solution Approach 2:
The system uses an intermediary layer (metadata and data structures) to carry annotation information without requiring direct visual modification of the image. This intermediary approach preserves image aesthetics while maintaining user awareness through alternative presentation methods such as tooltips, sidebars, or search interfaces.
Data Source
AI summary
A method and system for electronically annotating an image, or preferably, specific portions of an image, with information. In one aspect, a user may generate an image, annotate the image, and upload or otherwise disseminate the annotated image to a web page or the like. Annotations are provided as labels that provide information about the image or items therein. Further users may access the labels to access the information. The labels further enable searching and contextual advertising.