Image Source Ranking with Hierarchical Tag Expansion
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Solution Overview
Problem
Existing transfer learning methods struggle to identify appropriate datasets for training machine learning models due to the lack of detailed categorical information in large labeled datasets, leading to inefficiencies and resource wastage.
Innovation Solution
A system that extracts image tags from input images, expands these tags using a word embedding model, and queries a knowledge database to rank machine learning base models based on their relevance to the image content, capturing deeper hierarchies and improving the selection of training datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If transfer learning performance is estimated at the level of large conceptual categories, then the classification process is simplified, but the precision of model selection deteriorates due to loss of detailed hierarchical information
Solution Approach 1:
The patent segments the classification process into multiple hierarchical levels. Instead of treating all categories uniformly, it divides the category space into a hierarchy where broad conceptual categories are further segmented into subcategories and fine-grained categories. This allows the system to first perform coarse classification for ease of operation, then progressively refine the classification at lower hierarchical levels to achieve precise model selection.
Solution Approach 2:
The patent adds a hierarchical dimension to the classification process. By organizing categories into multiple levels (from broad to specific), it transforms a single-dimensional classification problem into a multi-dimensional hierarchical structure. This enables the system to operate efficiently at higher levels while capturing detailed information at lower levels, resolving the contradiction between simplicity and precision.
2Measurement precision
If detailed hierarchical categories are used to estimate transfer performance, then model selection precision is improved, but the complexity of the classification system increases
Solution Approach 1:
The patent segments the complex classification task into manageable hierarchical stages. Rather than implementing a single complex flat classification system, it divides the category space into hierarchical segments where each level handles a specific granularity of classification. This segmentation reduces the complexity at each individual stage while maintaining overall precision through the cumulative effect of hierarchical filtering.
Solution Approach 2:
The patent performs preliminary classification at higher hierarchical levels before proceeding to more detailed classification at lower levels. This preliminary action filters the data set progressively, so that by the time detailed hierarchical classification is applied, the scope has already been reduced. This approach achieves high precision without requiring the entire complex classification system to operate simultaneously at full detail.
3Measurement precision
If comprehensive image analysis with multiple tags and knowledge database queries is performed, then the accuracy of base model ranking is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary image tagging and category identification before conducting detailed knowledge database queries. By extracting initial tags and determining broad categories first, it prepares the data in advance so that subsequent detailed analysis can focus on refined subsets. This preliminary action reduces the overall processing time while maintaining ranking accuracy by ensuring that detailed queries are applied to pre-processed and organized data.
Solution Approach 2:
The patent segments the image analysis process into distinct stages: initial tag extraction, hierarchical category determination, knowledge database querying, and base model ranking. Each stage processes specific aspects of the analysis independently and in sequence. This segmentation allows computational resources to be distributed across stages rather than requiring simultaneous execution of all operations, reducing overall processing time while maintaining comprehensive analysis for accurate ranking.
Data Source
AI summary
A system for ranking machine learning base models for transfer learning purposes is described. The system receives image data in the form an image or an image set and extracts image tags from the images. The image tags are expanded into a set of associated terms using a word embedding database and model. The associated terms are used to query a knowledge database for parent or categorical terms used to rank various matching machine learning base models that may be improved or trained by the image data.