Image Captioning Augmented with Surrounding Text Context
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Solution Overview
Problem
Conventional image captioning systems fail to provide contextualization of images with respect to the surrounding text, leading to a lack of enriched and enhanced captions.
Innovation Solution
The system generates an augmented caption by creating a knowledge graph that combines an image caption graph and a contextual graph, using natural language understanding to recalculate node and edge importance, incorporating emotion, entity, and tone analysis to enhance the caption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image captioning systems are used, then image description accuracy is improved, but contextualization with surrounding text is lost
Solution Approach 1:
The patent merges the image caption graph (containing object detection and relationship information) with the contextual graph (containing surrounding text information) into a unified knowledge graph. This combination allows the system to preserve both accurate image description and contextual information from surrounding text, resolving the contradiction between description accuracy and contextualization.
Solution Approach 2:
The patent introduces a knowledge graph as an intermediary structure that bridges image content and surrounding text. The knowledge graph serves as a mediator that integrates visual information from the image caption graph with textual context from the contextual graph, enabling both accurate description and contextualization to coexist.
2Manufacturing precision
If manual caption creation is used, then caption quality is improved, but productivity is reduced
Solution Approach 1:
The patent implements an automated system that generates enhanced captions without manual intervention. The knowledge graph automatically integrates image content and surrounding text, and the natural language generation system autonomously produces contextualized captions, eliminating the need for manual caption creation while maintaining high quality.
Solution Approach 2:
The patent changes the parameters of automated caption generation by incorporating contextual information from surrounding text into the knowledge graph. This parameter change transforms the output from simple object detection to enriched contextualized descriptions, achieving manual-quality captions through automated processes.
3Productivity
If automated caption generation is used, then productivity is improved, but contextual understanding is reduced
Solution Approach 1:
The patent performs preliminary action by constructing the knowledge graph before generating captions. The system pre-integrates image content and surrounding text into the knowledge graph structure, establishing contextual relationships in advance. This preliminary contextualization enables fast automated generation of contextually understanding captions.
Solution Approach 2:
The patent replaces traditional mechanical caption generation (simple object detection) with a knowledge graph-based system that incorporates natural language understanding. This substitution enables automated systems to achieve contextual understanding by processing semantic relationships in the knowledge graph rather than relying on basic image recognition alone.
Data Source
AI summary
To augment an image caption, a caption graph containing entity nodes corresponding to entities contained in the image and relationship edges between entity nodes corresponding to relationships between entities as illustrated in the image is generated. In addition, a contextual graph containing one or more of entity nodes corresponding to entities contained in the image and described in text associated with the image, textual entity nodes corresponding to textual entities described in text associated with the image and textual relationship edges between entity node pairs, textual entity node pairs and entity node and textual entity node pairs is generated. The textual relationship edges correspond to relationships described in the text associated with the image between entity pairs, textual entity pairs or entity and textual entity pairs. From the contextual graph, an augmented caption graph containing entity nodes, relationship edges, textual entities and textual relationship edges is generated.