Hierarchical Model System for Image Classification
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Solution Overview
Problem
Current image analysis systems face challenges such as requiring large amounts of training data for single models, inaccurate classification, computational delays, inability to validate image information, limited classification options, and inefficiencies in resource distribution and model updates.
Innovation Solution
A network-based hierarchical model system that routes images through a series of classification models, where each model's output determines the next model to process the image, allowing for accurate and detailed classification while conserving computational resources and enabling easy updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single large model is used to handle all image analysis details, then comprehensive classification capability is improved, but computational resource requirements increase significantly
Solution Approach 1:
The patent divides a single large classification model into multiple smaller hierarchical models arranged in layers. Each model in the hierarchy handles specific classification tasks at different levels of detail, processing images in sequence from general to specific categories. This segmentation reduces the computational burden on any single model while maintaining comprehensive classification capability across the entire hierarchy.
2Adaptability or versatility
If a single large model is used to handle all image analysis details, then comprehensive classification capability is improved, but model complexity increases
Solution Approach 1:
The patent segments the complex classification task into multiple simpler models organized in a hierarchical structure. Each model in the hierarchy is less complex than a single comprehensive model would be, handling specific aspects of image classification at its level. This reduces individual model complexity while achieving comprehensive classification through the coordinated hierarchy.
Solution Approach 2:
The hierarchical model structure allows dynamic routing of images through the hierarchy based on classification needs. Models can be selectively activated or bypassed depending on the image characteristics and required classification depth, making the overall system adaptable without requiring all models to be maximally complex.
3Measurement precision
If more computation resources are allocated to image analysis, then classification accuracy is improved, but processing time increases
Solution Approach 1:
The patent segments the classification process into hierarchical stages, where simpler models perform initial classification to filter out cases that don't require extensive analysis. Only images requiring higher accuracy proceed to deeper levels of the hierarchy with more computational resources, thus maintaining accuracy for complex cases while reducing average processing time.
Solution Approach 2:
The hierarchical approach applies partial classification action at each level, where models process only the portion of classification detail appropriate to their level. This avoids excessive computation on all images by applying full analysis only where necessary, optimizing the balance between accuracy and processing time.
Data Source
AI summary
A computer system is provided and is programmed to: (1) receive a plurality of images; and/or (2) for each image of the plurality of images, the at least one processor is programmed to: (a) retrieve an image of the plurality of images; (b) execute a hierarchy of models with the retrieved image as input; (c) output classification information for the retrieved image based upon the execution; and/or (d) associate the classification information with the retrieved image.