Weighted k-NN Anomaly Detection for Imbalanced Image Features
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Solution Overview
Problem
Existing anomaly detection systems face challenges in distinguishing between good and bad objects due to data imbalance, with many good objects outweighing bad objects, leading to difficulties in constructing accurate models and issues like false anomaly classifications and missed detections.
Innovation Solution
A method involving a feature extractor neural network and a weighted k-nearest neighbors (k-NN) model with gradient ascent to iteratively update filter weights, enhancing separation between good and bad scores through threshold and model adaptation, and feature weighting to optimize filter distances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional k-NN anomaly detection is used with equal filter weights, then the system is simple to implement, but it cannot effectively distinguish between good and bad objects due to data imbalance
Solution Approach 1:
The patent changes the parameter of filter weights from uniform (all weights equal to 1) to optimized values through gradient ascent. This parameter change allows the system to differentiate between important and less important features, improving anomaly detection accuracy by emphasizing features that better separate good from bad objects despite data imbalance.
Solution Approach 2:
The patent performs preliminary optimization of filter weights using gradient ascent before final anomaly detection. By pre-optimizing the weighting parameters using training data, the system prepares the feature extraction process to better handle the data imbalance problem, allowing more effective anomaly detection when processing new objects.
2Measurement precision
If more training data for bad objects is collected to improve model accuracy, then anomaly detection precision improves, but the cost and time of data collection increase
Solution Approach 1:
Instead of collecting more bad object data, the patent changes the parameter space by introducing optimized filter weights. This allows the existing limited bad object data to be more effectively utilized by emphasizing the most discriminative features through gradient-based weight optimization, achieving better precision without additional data collection time.
Solution Approach 2:
The patent introduces filter weights as an intermediary parameter between the feature extraction process and anomaly scoring. These weights act as a mediator that amplifies the signal from limited bad object data by prioritizing features that are most indicative of anomalies, effectively bridging the gap caused by data scarcity.
3Reliability
If threshold is adjusted to reduce false positives, then false anomaly classifications decrease, but missed anomaly detections increase
Solution Approach 1:
The patent changes the parameters of filter weights through gradient ascent optimization, which indirectly optimizes the separation between good and bad object scores. This parameter change creates a more robust scoring distribution that reduces the trade-off between false positives and missed detections, as the optimized weights better capture the true characteristics of anomalies.
Solution Approach 2:
The patent implements feedback through gradient ascent, where the anomaly detection system continuously refines filter weights based on the performance feedback from training data. This feedback loop adjusts the feature weighting to maximize the gap between good and bad scores, creating a more reliable decision boundary that reduces both false positives and missed detections simultaneously.
Data Source
AI summary
A method and system for anomaly detection from object images. A feature extractor provides feature data characterizing part images, including good parts and bad parts. Training data for good parts is used to create a k-nearest neighbors (k-NN) model core set. The feature data includes hundreds of feature vectors, each having hundreds of filter dimensions. A weight value of one is initially assigned to each filter, and test data comprising some good and some bad parts is evaluated by a weighted k-NN module to determine an anomaly score from the weighted feature data. After all test images are evaluated, good and bad data points nearest a threshold are selected and a gradient ascent computation is performed to update the filter weights. Anomaly scoring and gradient ascent are performed iteratively until filter weights are identified which maximize the separation between good and bad scores, thereby eliminating missed detections and false anomalies.