Image Processing Method for Semantic Entity Tagging
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Solution Overview
Problem
Current image processing methods fail to provide deep understanding of image content, resulting in non-semantic labeling and inaccurate analysis, despite the use of external knowledge graphs, which also lead to non-semantic tags and shallow content understanding.
Innovation Solution
A method and device that determine the feature expression of an object in an image based on its type, using models like CNN for feature extraction, and associate entities with the object by matching these expressions with a knowledge graph, enabling deep understanding and structured semantic tagging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If external knowledge graphs are used for image labeling, then the identification effect of tags is improved, but the tags become non-semantic and the picture content cannot be deeply understood
Solution Approach 1:
The patent segments the image processing task into multiple stages: initial object detection, feature extraction, knowledge graph matching, and semantic relationship verification. This segmentation allows the system to maintain high identification accuracy while recovering semantic meaning by processing different aspects of the image content separately and combining results.
Solution Approach 2:
The patent introduces an intermediary verification mechanism that acts as a mediator between the knowledge graph matching results and the final tagging output. This intermediary layer validates and enriches the tags by cross-referencing multiple data sources and semantic relationships, thereby restoring semantic meaning while maintaining identification accuracy.
2Productivity
If shallow analysis methods are used for image processing, then the processing speed is improved, but the analysis results are non-semantic and inaccurate
Solution Approach 1:
The patent performs preliminary feature extraction and object detection using optimized algorithms that provide fast initial results. These preliminary results are then refined through subsequent semantic verification steps, allowing the system to maintain high processing speed while achieving accurate semantic analysis through multi-stage processing.
Solution Approach 2:
The patent dynamically adjusts processing parameters based on the complexity of the image content. For simple images, faster shallow analysis methods are used; for complex images requiring deep understanding, the system increases processing depth and applies more rigorous semantic verification, thereby optimizing the balance between speed and accuracy.
Data Source
AI summary
Embodiments of the present provide a method and a device for processing an image, server and storage medium. The method includes: determining, based on an object type of an object in an image to be processed, a feature expression of the object in the image to be processed; and determining an entity associated with the object in the image to be processed based on the feature expression of the object in the image to be processed and a feature expression of an entity in a knowledge graph.