Granular Image Entity Extraction and Annotation
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Solution Overview
Problem
Current content management systems do not allow for granular interactivity with specific elements in images, limiting users' ability to provide personalized reactions or comments on individual entities within an image.
Innovation Solution
A method that identifies distinct elements in an image, generates sub-images, and allows users to interact with these elements by displaying descriptive adjectives and annotation data, which can be overlaid on the image, enabling seamless and implicit interaction with specific entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users interact with images as a whole, then the system is simple to operate, but granular interactivity with specific elements is lost
Solution Approach 1:
The system segments the image into multiple distinct elements or objects, allowing users to interact with individual elements rather than the entire image. This is achieved through automated element detection and creation of interactive hotspots on identified objects, enabling granular engagement while maintaining ease of use.
Solution Approach 2:
The system introduces an intermediary layer of element identification and hotspot generation between the user and the image content. This intermediary automatically detects objects, creates interactive zones, and manages annotation data, bridging the gap between simple image display and complex granular interaction.
2Adaptability or versatility
If the system identifies and processes multiple distinct elements in an image, then granular interactivity is enabled, but the device complexity increases
Solution Approach 1:
The system performs self-service by automatically identifying elements, generating sub-images, creating hotspots, and assigning annotations without requiring manual configuration. The automated element detection and classification processes eliminate the need for manual setup, reducing operational complexity despite enabling granular interactivity.
Solution Approach 2:
The system implements a universal element detection and interaction framework that can handle multiple types of image elements (objects, faces, scenes) through a single integrated process. This multi-functional approach allows the same system architecture to serve various interaction needs without requiring separate specialized components for each element type.
3Adaptability or versatility
If annotation data is assigned to each distinct element, then personalized reactions are enabled, but the quantity of data to be managed increases
Solution Approach 1:
The system applies local quality by associating specific annotation data and adjectives with individual elements rather than uniformly processing the entire image. Each element receives tailored annotations based on its specific characteristics, allowing personalized reactions to be displayed contextually for each detected object while managing data volume through selective processing.
Data Source
AI summary
Methods, computer program products, and/or systems are provided that perform the following operations: identifying two or more distinct elements in an image; generating a sub-image for each of the two or more distinct elements; generating adjectives descriptive of content associated with a distinct element for each sub-image; displaying a response list including the adjectives associated with the distinct element of a selected sub-image in response to an interaction with the image; obtaining annotation data based in part on the response list displayed for the distinct element of the selected sub-image; and assigning the annotation data to the distinct element of the selected sub-image, wherein the annotation data is displayed in response to an interaction with the distinct element in the image.