Region-Based Image Metadata Indexing for Precise Search
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Solution Overview
Problem
Existing image management systems lack the ability to provide detailed, region-specific metadata, leading to limited search and retrieval capabilities and suboptimal utilization of images in various contexts.
Innovation Solution
A system that allows users to input free-form region selections and descriptions, associating them with specific image regions, enabling rich datasets that facilitate improved search querying and image collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional image management systems store images with basic metadata only, then storage simplicity is maintained, but search and retrieval capabilities are limited
Solution Approach 1:
The patent segments the image into multiple regions of interest (ROIs) and assigns specific metadata to each region rather than treating the image as a whole. This segmentation allows detailed information to be stored for specific areas (e.g., defects, features) without requiring complex metadata for the entire image, thus improving information retention while managing system complexity through structured regional decomposition.
Solution Approach 2:
The system applies local quality by associating different types and levels of metadata detail with different regions of the image based on their importance. Critical regions receive detailed metadata while less important regions use simpler metadata, optimizing the balance between information completeness and system complexity.
2Measurement precision
If region-specific metadata is implemented for all image areas, then search precision is improved, but data processing complexity increases
Solution Approach 1:
The system performs preliminary action by automatically identifying and segmenting regions of interest before metadata assignment. This pre-processing step identifies which regions require detailed metadata, reducing the overall complexity compared to annotating every region while maintaining high search precision for critical areas.
Solution Approach 2:
The system applies partial action by focusing metadata annotation efforts only on regions of interest rather than the entire image. This selective approach achieves high search precision for relevant areas while avoiding the excessive complexity that would result from comprehensive region-by-region metadata assignment across the whole image.
3Reliability
If detailed region-specific metadata is stored for each image, then image retrieval accuracy is enhanced, but storage requirements increase
Solution Approach 1:
By segmenting the image into regions and storing metadata only for relevant regions rather than the entire image, the system achieves high retrieval accuracy for critical areas while significantly reducing the total metadata data volume compared to comprehensive whole-image annotation.
Solution Approach 2:
The system applies local quality by varying the level of metadata detail across different regions, providing detailed information only where necessary for accurate retrieval while using minimal or no metadata for less important regions, thus optimizing the balance between retrieval accuracy and storage requirements.
Data Source
AI summary
An embodiment provides a method, including: receiving, from a device, a user identification; storing, in a storage device, first data produced by first free-form user input specifying one or more regions of an image and second data comprising second free-form user input describing the one or more regions of the image; each first data having corresponding second data stored in association therewith; selecting, using a processor, a data set comprising at least a portion of the first data and the second data based at least in part on the user identification; and providing the selected data set for display. Other embodiments are described and claimed.