Neural Network Classification Device Using Convolutional Feature Integration
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Solution Overview
Problem
Existing techniques for classifying targets using neural networks struggle with integrating image values and attribute parameters effectively, as simple averaging methods provide inadequate discrimination performance, and weighted mean values are difficult to determine accurately.
Innovation Solution
A classification device and method that uses a neural network with a convolution operator to convolve individual elements of a feature map with attribute parameters, allowing for the automatic adjustment of weight coefficients through backpropagation, thereby integrating image data and attribute parameters seamlessly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If simple average integration method is used to combine image values and attribute parameters, then the integration process is simple, but the discrimination performance is inadequate
Solution Approach 1:
The patent changes the integration method from simple averaging to a learned integration process using a neural network. The network learns optimal integration parameters (weights and transformations) during training, dynamically adjusting how image values and attribute parameters are combined to maximize discrimination performance while maintaining operational simplicity through automated learning.
Solution Approach 2:
The patent introduces a neural network as an intermediary component between image values and attribute parameters. This intermediary automatically learns the optimal integration strategy, transforming both inputs into a unified feature representation that achieves high discrimination performance without requiring manual weight tuning or complex integration logic.
2Measurement precision
If weighted mean value integration is used to improve discrimination performance, then the classification accuracy improves, but determining appropriate weights becomes difficult
Solution Approach 1:
The patent implements self-service by enabling the neural network to automatically determine optimal weights and integration parameters through backpropagation and gradient descent during training. The system learns the appropriate weighting scheme from data without requiring external manual intervention or expert knowledge, thereby achieving high classification accuracy while eliminating the complexity of manual weight determination.
Solution Approach 2:
The patent employs feedback mechanisms through the neural network's loss function and backpropagation process. The network receives feedback from classification performance metrics and continuously adjusts integration weights and parameters to minimize error, automatically converging to optimal weight values that maximize classification accuracy without manual intervention.
Data Source
AI summary
A classification device for classifying targets using a neural network on the basis of a target image that captures each target and at least one attribute parameter associated with the target. The classification device is equipped with a receiver, a neural network unit, and a classifier. The receiver receives a target image that captures a target and at least one attribute parameter associated with the target. The classifier classifies targets using the neural network unit. In the neural network unit, a convolution operator convolves individual elements of a provided feature map and the received at least one attribute parameter.