Comment-Object Correlation in Image Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current image management technologies fail to accurately correlate comments with objects in images, limiting user interaction and sentiment analysis for targeted advertising.
Innovation Solution
A method that uses cognitive and contextual analysis to detect and identify objects in images, determine which objects are referred to in comments, and link comments with the relevant objects, employing natural language processing and machine learning for enhanced accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image management techniques are used, then basic image storage and sharing are achieved, but accurate correlation between comments and objects in images cannot be accomplished
Solution Approach 1:
The system segments the image into multiple detected objects first, then performs cognitive and contextual analysis to match comments with specific objects. This segmentation approach allows precise comment-object correlation without requiring a single complex analysis system, as each object can be independently analyzed and matched with relevant comments.
Solution Approach 2:
The patent introduces an intermediary cognitive and contextual analysis layer between image processing and comment correlation. This intermediary analyzes the semantic meaning of comments and matches them with detected objects, resolving the contradiction by adding a specialized component rather than redesigning the entire system.
2Measurement precision
If cognitive and contextual analysis is performed on comments, then accurate object identification is achieved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary detection of objects in the image before analyzing comments. By pre-identifying and segmenting objects, the cognitive and contextual analysis only needs to match comments with already-detected objects rather than performing full image analysis, significantly reducing processing time while maintaining accuracy.
Solution Approach 2:
The system applies cognitive and contextual analysis selectively only to comments that require object correlation, rather than analyzing all image data. This partial action approach focuses computational resources on the specific task of comment-object matching, reducing overall processing time while achieving accurate identification.
3Loss of information
If objects are detected and identified in images, then basic object recognition is achieved, but correlation with user comments and sentiment analysis cannot be performed
Solution Approach 1:
The system incorporates feedback loops where detected objects and their attributes feed into cognitive analysis of comments, which then feeds back to refine object identification and correlation. This feedback mechanism enables automated sentiment analysis and comment-object correlation by continuously refining the matching process based on contextual information from user interactions.
Solution Approach 2:
The patent creates a multi-functional system that performs object detection, comment analysis, sentiment extraction, and correlation in a single integrated process. This universal approach captures user sentiment and interaction data while maintaining a high level of automation, as the same system handles multiple tasks rather than requiring separate specialized systems.
Data Source
AI summary
An approach is provided for correlating a comment about an image with first object(s) in the image. Object(s) in the image are detected and identified. Based on a cognitive and contextual analysis of the comment, the first object(s) included in the identified object(s) are determined to be referred to by the comment. Responsive to determining that the first object(s) are referred to by the comment, the comment is linked with the first object(s). Alternately, based on a cognitive and contextual analysis of the comment about the image, the comment is determined to be referring to object(s). Responsive to determining that the comment refers to the object(s), the image is scanned and responsive to the image being scanned, the object(s) are identified in the image. Based on the comment referring to the object(s) and responsive to the object(s) being identified, the comment is linked with the identified object(s).