Visual Media Tagging via Computer Vision and Metadata Refinement
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Solution Overview
Problem
The manual tagging of visual media items, such as photos and videos, is time-consuming and often not feasible due to the large volume of media collected, leading to incomplete or missing tags, which limits the ability to search and organize visual media effectively.
Innovation Solution
Automated tagging of visual media items using computer vision analysis to generate a set of tags with associated probabilities, which can be refined based on independent information such as metadata and user interactions, facilitating efficient search and organization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual tagging of visual media items is performed, then search and organization capability is improved, but time consumption and user effort increase significantly
Solution Approach 1:
The system enables visual media items to tag themselves automatically through computer vision analysis. The computer-generated tags are produced without human intervention by analyzing image content, EXIF data, and sensor metadata, allowing the media items to self-organize and self-describe, thereby eliminating the time-consuming manual tagging process while maintaining reliable search and organization capabilities
Solution Approach 2:
The patent replaces the mechanical manual tagging process with an automated computer vision system. Instead of users manually assigning tags, the system uses image analysis algorithms, EXIF data processing, and sensor metadata analysis to automatically generate tags, substituting human labor with computational processes that are faster and more scalable
2Productivity
If computer vision analysis is used to generate tags automatically, then tagging efficiency is improved, but tag accuracy may be insufficient without manual verification
Solution Approach 1:
The system merges multiple data sources including computer vision analysis results, EXIF metadata, and sensor metadata to generate comprehensive tags. By combining information from image content analysis with contextual data from device sensors and photo properties, the system achieves both high productivity through automation and improved accuracy through multi-source validation
Solution Approach 2:
The system incorporates user interactions as feedback to continuously improve tag accuracy. When users interact with tagged media items, this feedback is used to refine and re-rank tags, allowing the system to learn from user behavior and progressively improve tagging precision while maintaining high automation levels
3Measurement precision
If multiple data sources are integrated to refine tags, then tag accuracy is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary analysis of multiple data sources (computer vision results, EXIF data, sensor metadata) during the initial tagging process. By pre-processing and integrating these diverse data sources upfront, the system establishes a comprehensive tag foundation that reduces the need for complex ongoing processing and simplifies the overall system architecture while maintaining high tag accuracy
Data Source
AI summary
In one embodiment, a set of tags that has been generated by performing computer vision analysis of image content of a visual media item may be obtained, where each tag of the set of tags has a corresponding probability. In addition, a set of information that is independent from the image content of the visual media item may be obtained. The probability of at least a portion of the set of tags may be modified based, at least in part, upon the set of information.