Image Metadata Capture via Dynamic Section Matching
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Solution Overview
Problem
Conventional methods for collecting metadata about images are limited to the entire media object and do not allow for detailed metadata capture or statistical evaluation of specific image areas.
Innovation Solution
A method where images are divided into predetermined or post-determined sections, allowing respondents to select and input metadata for freely chosen image areas, which are then compared to these sections for statistical evaluation, enabling detailed data processing and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If metadata collection is limited to the entire image object, then the survey method remains simple and easy to operate, but the detail and precision of metadata capture is insufficient
Solution Approach 1:
The image is divided into multiple predetermined image sections (e.g., quadrants or grid sections) before the survey begins. Respondents select and provide metadata for specific sections rather than the entire image, enabling detailed area-specific analysis while maintaining a simple survey interface. The system automatically compares selected areas with predetermined sections and assigns metadata to the matching section.
2Measurement precision
If the image is divided into predetermined sections and respondents must select from these sections, then detailed area-specific metadata can be collected, but respondents lose freedom in selecting image areas
Solution Approach 1:
The system accepts dynamic, freely selected image areas from respondents without pre-defining selection boundaries. After collection, the system dynamically compares these freely selected areas with predetermined image sections using automated image processing to determine spatial coincidence and assign metadata appropriately. This maintains respondent freedom while enabling structured analysis.
3Loss of information
If statistical evaluation is performed on the entire image metadata, then the evaluation process is simple, but the ability to analyze specific image areas is lost
Solution Approach 1:
The evaluation process segments metadata analysis by predetermined image sections. The system generates a database that stores both the selected image areas and corresponding predetermined section assignments, enabling statistical evaluations to be performed separately for each image section. This allows area-specific insights while maintaining an automated, manageable evaluation workflow.
4Quantity of substance
If multiple metadata entries are collected for different image areas, then comprehensive coverage is achieved, but data processing and matching complexity increases
Solution Approach 1:
The system employs automated self-service mechanisms where the computer automatically compares freely selected image areas with predetermined sections, determines spatial coincidence relationships, and assigns metadata to appropriate sections without manual intervention. This automation handles the complexity of processing multiple metadata entries while maintaining system efficiency.
Data Source
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AI summary
The method involves displaying an image of a set of interviewing persons. An input of meta data (130c) is collected to the image. The image is divided into predetermined and postdetermined image sections, where the input of the metadata to a region of the image freely selectable by an interviewing person is detected. The freely selected image region is compared with predetermined and /or postdetermined image sections, and the metadata is assigned to the matched, pre or post-determined image sections during the occurrence of a match over a predetermined measure. An independent claim is also included for a device for determining and supplying information to an image in a interview.