Image Element Hierarchy Parsing Without Metadata or Post-Processing
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Solution Overview
Problem
Existing image processing technologies struggle to universally parse the hierarchical relationships of elements in images, especially in the absence of metadata, and require additional post-processing to determine higher-level relationships.
Innovation Solution
A recursive image parsing model that decomposes input images into atomic elements, generating hierarchies by detecting next-level elements based on features and tokens, without the need for additional data or post-processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If existing image processing technologies are used to parse hierarchical relationships, then the process requires additional post-processing steps, but this increases the complexity and time consumption of the overall system
Solution Approach 1:
The patent applies preliminary action by training the neural network model in advance to perform both element detection and hierarchical relationship determination simultaneously. The model is pre-trained to output not only detected elements but also their hierarchical relationships directly, eliminating the need for separate post-processing steps to determine hierarchical relationships.
2Measurement precision
If metadata is required to determine hierarchical relationships, then the accuracy of hierarchy parsing is improved, but this reduces the versatility of the solution across different image types
Solution Approach 1:
The patent applies self-service by enabling the neural network model to automatically determine hierarchical relationships based solely on image content and detected element positions, without requiring external metadata. The model learns to infer hierarchical structures from visual features and spatial relationships within the image itself, making it self-sufficient and applicable to various image types including UI images, natural images, and documents.
3Adaptability or versatility
If a universal image parsing solution is developed that works without metadata, then the adaptability to different image types is improved, but the precision of hierarchical relationship determination deteriorates
Solution Approach 1:
The patent applies parameter changes by modifying the neural network model's architecture and training parameters to simultaneously achieve universality and precision. The model uses multi-scale feature extraction, attention mechanisms, and loss function design that balances both accurate hierarchical relationship determination and adaptability across different image types without requiring metadata.
Data Source
AI summary
According to the implementation of the present disclosure, a solution for parsing the hierarchical relationship of elements in an image is provided. According to the solution, the second element in the first element is determined based on a feature(s) of the input image and the first element in the input image. The third element in the second element is detected based on the feature and the second element. The first element, the second element and the third element correspond to corresponding regions in the input image. Based on the determination of the second element and the detection result of the third element, a hierarchy indicating the relationship between elements in the input image is determined. In this way, the hierarchy of elements in the image can be obtained without post-processing.