Image Recognition Matrix Alignment for Faster, More Accurate Recognition
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Solution Overview
Problem
The inconsistency of dimensions in the operation matrix of a fully connected layer in current image recognition technologies leads to a complicated operation process, resulting in low accuracy and slow recognition speeds.
Innovation Solution
The method involves adjusting a first recognition model using a second recognition model with an input layer, fully connected layer, and recognition layer, and enhancing the dimension of an initial feature matrix to match the weight matrix dimension for efficient feature extraction and recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the dimension of the operation matrix in the fully connected layer is increased to improve recognition accuracy, then the recognition accuracy improves, but the computational complexity and operation time increase
Solution Approach 1:
The patent performs dimension consistency checking and matrix operation preparation in advance before the main recognition process. By pre-validating dimension compatibility and preparing operation matrices, the system avoids costly runtime errors and optimizations during actual recognition, thus improving accuracy without proportionally increasing recognition time
Solution Approach 2:
The patent implements a feedback mechanism that monitors dimension consistency throughout the matrix operations. When dimension mismatches are detected, the system automatically adjusts or validates the operation parameters, ensuring accurate computations while maintaining efficient processing through iterative optimization
2Measurement precision
If the dimension of the operation matrix is increased to improve recognition accuracy, then the recognition accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent segments the fully connected layer operations into multiple smaller matrix operations with consistent dimensions. By breaking down large-scale matrix multiplications into manageable segments with validated dimensions, the system achieves high recognition accuracy through cumulative processing while reducing the complexity of individual operation steps
Solution Approach 2:
The patent dynamically adjusts matrix operation parameters such as dimension sizes, batch processing sizes, and operation granularity. By optimizing these parameters to ensure dimension consistency, the system improves recognition accuracy while maintaining computational efficiency through parameter-tuned operations
3Productivity
If the dimension of the operation matrix is inconsistent, then the operation process becomes complicated, but reducing the dimension would limit recognition capability
Solution Approach 1:
The patent establishes dimension consistency as a baseline requirement across all matrix operations in the fully connected layer. By ensuring that all operation matrices maintain compatible dimensions through validation and adjustment mechanisms, the system creates an equipotential operational environment where recognition processes flow smoothly without complexity-inducing dimension mismatches, thereby improving recognition speed
Data Source
AI summary
An image recognition method applied to an electronic device is provided. The method includes obtaining a recognition region and a plurality of test regions. A plurality of first prediction results is obtained by predicting each of the plurality of test regions using a first recognition model. A prediction accuracy rate is calculated. A plurality of target regions is obtained from the plurality of test regions, and a second recognition model is obtained by adjusting the first recognition model based on the prediction accuracy rate and the plurality of target regions. An initial feature matrix is obtained by inputting the recognition region in the second recognition model. A target vector is generated according to a target feature matrix and an initial weight matrix; and a recognition result of the image to be recognized is obtained by inputting the target vector into the second recognition model.