Semantic Label Model for Digital Image Recognition
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Solution Overview
Problem
Existing image recognition technologies are insufficient in providing detailed information about digital images, requiring manual recognition of features and lacking accuracy in assigning semantic labels, which limits their application in recognizing and describing image content.
Innovation Solution
A method and apparatus that utilize a semantic label model to correlate digital images with semantic labels, enabling the extraction of full-image and local recognition information to form a comprehensive semantic label, improving the accuracy and detail of image description.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing image recognition technology is used to recognize digital images, then basic image content can be identified, but detailed information (such as breed and color of animals) cannot be provided
Solution Approach 1:
The patent divides the image recognition process into two distinct stages: full-image recognition to obtain overall image information, and local recognition of specific regions of interest to extract detailed features. This segmentation allows the system to capture both general content and fine-grained details without overwhelming the recognition system, thereby reducing information loss while maintaining recognition precision.
Solution Approach 2:
The patent introduces a hierarchical dimension to the recognition process by organizing recognition results into multiple levels: full-image level for overall content and local region level for detailed features. This dimensional expansion enables the system to provide comprehensive information at different granularities, simultaneously addressing both information completeness and recognition accuracy requirements.
2Loss of information
If manual recognition is used to obtain detailed image features, then detailed information can be extracted, but the process is time-consuming and inefficient
Solution Approach 1:
The patent enables the system to automatically perform both full-image recognition and local region recognition without human intervention. The system self-identifies regions of interest, automatically extracts local features, and integrates results to produce comprehensive detailed information, thereby eliminating the need for manual recognition while maintaining high information extraction quality and improving efficiency.
Solution Approach 2:
The patent performs full-image recognition first to identify potential regions of interest before conducting detailed local recognition. This preliminary action guides the subsequent local recognition process, allowing the system to focus computational resources on relevant areas and automatically extract detailed information more efficiently than manual methods.
3Adaptability or versatility
If semantic labels are assigned based on extracted image features or manually selected areas, then some level of description can be achieved, but accurate semantic labels are difficult to provide and the method is hard to apply widely
Solution Approach 1:
The patent merges the results of full-image recognition and local region recognition to form comprehensive semantic labels. By combining overall image context with detailed local features, the system generates accurate semantic descriptions that are both precise and broadly applicable across different image types and scenarios.
Solution Approach 2:
The patent uses the full-image recognition results to guide and refine the local recognition process, creating a feedback loop where overall context informs detailed analysis. This feedback mechanism improves semantic label accuracy by ensuring local features are interpreted within their proper contextual framework, while maintaining broad applicability across diverse image content.
Data Source
AI summary
The present application discloses a method and apparatus for obtaining a semantic label of a digital image. An implementation of the method includes: obtaining the digital image; looking up a semantic label model corresponding to the digital image, the semantic label model being used for representing correlation between digital images and semantic labels, and a semantic label being used for literally describing a digital image; and introducing the digital image into the semantic label model to obtain full-image recognition information and local recognition information corresponding to the digital image, and combining the full-image recognition information and the local recognition information to form a semantic label, the full-image recognition information being a summarized description of the digital image, and the local recognition information being a detailed description of the digital image. According to the implementation, the digital image is obtained first, then a semantic label model corresponding to the digital image is looked up, and a semantic label is obtained by using the semantic label model, which may improve the accuracy of obtaining the semantic label corresponding to the digital image.