Weighted Digital Image Object Tagging via Relative Weighting
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Solution Overview
Problem
Existing image tagging methods rely on pre-defined tags, which are limited, inflexible, and prone to errors, failing to accurately represent user browsing behavior and preferences across different platforms.
Innovation Solution
A computer-implemented method that automatically tags digital images based on relative weighting of recognized objects, using object recognition technology and weighting factors like object core point, scale, and focus, to generate auto-tags and build user preference profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pre-defined tags are used for image tagging, then the tagging process is simple and fast, but the tags are limited, inflexible, and fail to accurately represent user browsing behavior and preferences
Solution Approach 1:
The system performs self-service by automatically generating tags through object recognition technology without requiring manual pre-definition. The algorithm autonomously analyzes image content, identifies objects, and generates descriptive tags, eliminating the need for human annotators to create and maintain extensive tag dictionaries while improving accuracy in representing user preferences
Solution Approach 2:
The system changes the parameter of tag generation from static pre-defined categories to dynamic content-based descriptions. By using object recognition to identify and weight objects within images, the system generates tags that adapt to the specific content of each image, thereby accurately representing user browsing behavior without being constrained by fixed tag sets
2Reliability
If pre-defined tags are used for image tagging, then the system implementation is straightforward, but the tags are prone to errors and fail to capture detailed object information
Solution Approach 1:
The system replaces the manual mechanical process of defining and assigning tags with an automated computer vision system. Object recognition technology automatically analyzes image content, identifies objects, determines their prominence through weighting, and generates tags without human intervention, thereby improving reliability while maintaining ease of implementation through automated processing
Solution Approach 2:
The system introduces object recognition technology as an intermediary between the image and the tag. This intermediary automatically analyzes image content, identifies objects, and generates descriptive tags, eliminating the need for manual tag definition and reducing errors associated with human annotation while keeping the system implementation straightforward
3Measurement precision
If automatic object recognition and weighting is implemented, then accurate and flexible tags are generated, but the processing time and computational resources increase
Solution Approach 1:
The system applies local quality by focusing computational resources on identifying and weighting only the most prominent objects in each image rather than analyzing every detail equally. By determining object prominence through weighting mechanisms and generating tags based on key objects, the system achieves precise tagging while reducing overall processing time and computational resource requirements
Data Source
AI summary
Weight image object tagging includes acquiring digital images based on a user browsing webpages, automatically tagging the digital images based on weighting individual image objects, where automatically tagging a digital image of the digital images is based on a relative weighting between objects recognized from that digital image, and building a user preference profile based on recurrences of tags across the digital images.