Orthogonal Classifiers for High-Precision Object Categorization
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Solution Overview
Problem
Conventional classification algorithms, such as Sparse Linear Regression (SLR), struggle with accurately categorizing novel objects and maintaining high precision without significantly lowering recall, as they only learn distinguishing characteristics between classes and not the inherent features of objects, leading to false positives and the exclusion of true positives.
Innovation Solution
The use of two orthogonal classifiers with relaxed quality thresholds, where each classifier applies a similar algorithm or rule set, allowing for the categorization of objects only if both classifiers match in their results, thereby reducing false positives and improving recall by accommodating novel objects and maintaining high precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a threshold confidence level is set to reduce false positive classifications, then precision is improved, but recall deteriorates as many true positives are eliminated
Solution Approach 1:
The patent divides the classification system into multiple independent classifiers (e.g., SLR classifier, template matching classifier, neural network classifier) that operate in parallel. Each classifier processes the input data independently and produces separate classification results, allowing the system to segment the classification task into multiple specialized components rather than relying on a single classifier with a high threshold.
Solution Approach 2:
The patent combines the results from multiple independent classifiers through a voting mechanism or result aggregation. By merging the classification outputs from different classifiers that have different strengths and weaknesses, the system achieves both high precision and high recall, as the combination compensates for the limitations of individual classifiers.
2Manufacturing precision
If SLR learns only distinguishing characteristics between classes, then it achieves good separation between known classes, but it fails to detect novel objects and produces false positives
Solution Approach 1:
The patent employs multiple classifiers with different functionalities: SLR provides discriminative classification for known classes, template matching provides similarity-based classification that can handle novel objects, and neural networks provide feature-based classification. This multi-functional approach allows the system to handle both familiar and novel objects effectively.
Solution Approach 2:
The patent changes the parameters and characteristics of different classifiers to suit different classification needs. SLR uses linear combinations of features with learned weights, template matching uses correlation coefficients and similarity metrics, while neural networks use non-linear transformations. By varying the parameters and approaches of different classifiers, the system achieves both precise separation of known classes and detection of novel objects.
3Measurement precision
If a single classifier is used with high precision requirements, then false positives are reduced, but the system requires more manual resources and has lower productivity
Solution Approach 1:
The patent segments the classification workload across multiple specialized classifiers, allowing parallel processing of classification tasks. This segmentation enables the system to maintain high precision through multiple checks while increasing overall productivity by distributing the computational load across different classification algorithms that can operate simultaneously.
Solution Approach 2:
The system uses automated result aggregation and voting mechanisms that self-manage the classification process without requiring manual intervention. The multiple classifiers automatically validate and cross-check each other's results, providing self-service quality assurance that maintains high precision while eliminating the need for manual review resources.
Data Source
AI summary
A system categorizes one or more objects based at least in part upon one or more characteristics associated therewith. A first classifier includes a rule set to determine if each of the one or more objects meets or exceeds a quality threshold. A second classifier, orthogonal to the first classifier, includes a rule set to determine if each of the one or more objects meets or exceeds a quality threshold. In one embodiment, the quality threshold associated with the first classifier and the quality threshold associated with the second classifier are less than a predetermined target threshold. The result for each object of the first classifier is compared to the result of the second classifier. The object is categorized if the result of the first classifier and the result of the second classifier match. The object is uncategorized if the result of the first classifier does not match the result of the second classifier.