Two-Stage Neural Network Classifier for Image Recognition
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Solution Overview
Problem
Conventional image recognition techniques require increased neural network size and calculation, leading to reduced processing speed and accuracy due to the connection of multiple detailed class classifiers to a coarse class classifier, and errors in coarse class classification can result in incorrect detailed classification.
Innovation Solution
A method involving a two-stage learning process where a coarse class classifier and a detailed class classifier are trained separately using neural networks with adjusted final layers to maintain network size, allowing for high-accuracy image recognition without reducing processing speed, by learning features common to coarse and detailed classes respectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple detailed class classifiers are connected to a coarse class classifier, then classification accuracy is improved, but processing speed is reduced
Solution Approach 1:
The patent segments the classification task into two independent stages: coarse class classification and detailed class classification. Each stage has its own dedicated neural network classifier, avoiding the need to connect multiple detailed class classifiers to a coarse class classifier. This segmentation maintains classification accuracy while improving processing speed by eliminating redundant computational paths.
2Measurement precision
If multiple detailed class classifiers are connected to a coarse class classifier, then classification accuracy is improved, but device complexity is increased
Solution Approach 1:
The patent divides the classification system into two separate, independent neural network classifiers: a coarse class classifier and a detailed class classifier. This segmentation reduces device complexity by eliminating the complex interconnections required in traditional multi-classifier systems, while maintaining high classification accuracy through the two-stage classification approach.
Solution Approach 2:
The patent extracts the classification function into two distinct components, each handling a specific level of classification granularity. By taking out the detailed class classification from the coarse class classification framework and making it an independent system, the overall device complexity is reduced while preserving classification accuracy.
3Measurement precision
If neural network size is increased to improve classification accuracy, then measurement precision is improved, but processing speed is reduced
Solution Approach 1:
The patent segments the classification task into two independent neural networks of moderate size, each optimized for its specific classification level. This avoids the need for a single large neural network, thereby maintaining classification accuracy while improving processing speed by reducing the computational burden on any single network.
Data Source
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AI summary
Learning method includes performing a first process in which a coarse class classifier configured with a first neural network is made to classify a plurality of images given as a set of images each attached with a label indicating a detailed class into a plurality of coarse classes including a plurality of detailed classes and is then made to learn a first feature that is a feature common in each of the coarse classes, and performing a second process in which a detailed class classifier, configured with a second neural network that is the same in terms of layers other than the final layer as but different in terms of the final layer from the first neural network made to perform the learning in the first process, is made to classify the set of images into detailed classes and learn a second feature of each detailed class.