Hierarchical Image Classifier with Confidence Thresholds
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Solution Overview
Problem
Conventional data classification systems struggle with hierarchical multiclass image classification, particularly in scenarios where fine-grained classifications are desired but confidence levels are low, leading to a lack of reliable degradation to a known hierarchy.
Innovation Solution
The implementation of a progressive precision image classifier that trains a neural network to generate classification vectors with confidence values, calculates fine-grained and coarse-level confidence thresholds, and applies post-processing logic to refine classifications within a hierarchical structure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional image classification systems are used, then classification speed is maintained, but the system cannot reliably determine parent classifications when child classification confidence is low
Solution Approach 1:
The classification system is segmented into multiple hierarchical levels (parent classes and child classes). The neural network outputs confidence scores for multiple classification levels simultaneously, allowing the system to selectively apply fine-grained or coarse-grained classification based on confidence thresholds, thus improving reliability without requiring a completely complex system redesign
Solution Approach 2:
The system dynamically adjusts its classification granularity based on confidence levels. When confidence in fine-grained classification is sufficient, the system outputs detailed child class classifications; when confidence is low, it gracefully degrades to parent class classifications. This dynamic adaptation improves reliability while maintaining manageable system complexity
2Measurement precision
If fine-grained classification is pursued, then classification precision is improved, but development time and training complexity increase
Solution Approach 1:
The system performs preliminary classification at multiple hierarchical levels simultaneously during the forward pass. Confidence thresholds for both parent and child classes are pre-calculated and stored. This preliminary preparation enables rapid inference without requiring separate training processes for different granularity levels, thus achieving high precision without proportional increases in development time
Solution Approach 2:
A single neural network model serves multiple classification functions (both fine-grained and coarse-grained classification) through multi-label output. The model is trained once to produce confidence scores for all classification levels, making the system universal and avoiding the need for multiple separate models, thereby reducing development time while maintaining precision
3Adaptability or versatility
If multi-level hierarchical classification is implemented, then classification versatility is improved, but system complexity increases
Solution Approach 1:
The system adds a hierarchical dimension to the classification output space. Instead of a single classification label, the network outputs a structured vector containing confidence scores for multiple hierarchical levels. This dimensional expansion enables versatile classification (both fine and coarse grain) while managing complexity through a unified mathematical framework rather than multiple separate systems
4Measurement precision
If confidence thresholds are calculated for all classifications, then prediction accuracy is improved, but processing time during inference increases
Solution Approach 1:
All confidence threshold calculations are performed in advance during the validation phase and stored for runtime use. During inference, the system simply compares neural network output confidence scores against pre-computed thresholds, avoiding real-time threshold calculation. This preliminary preparation ensures high prediction accuracy while maintaining fast inference speed
Data Source
AI summary
Progressive precision image classifier and method of training include storing a dataset of labeled images, training a neural network to generate a classification vector comprising a plurality of confidence values, each confidence value corresponding to a classification, validating the trained neural network, calculating fine-grained confidence thresholds for each classification, wherein each classification represents a leaf-level classification in a hierarchical classification structure, and calculating coarse-level confidence thresholds for at least one parent class in the hierarchical classification structure, wherein each parent class defines a group of at least one leaf-level classification. Each label in the training data identifies a leaf-level classification in the hierarchical classification structure, and the classification vector includes a 1×N vector of confidence values, where N represents a number of leaf-level classifications output by the trained neural network. The neural network may be implemented as a convolution neural network with a single output head.