CNN Image Classification Automation
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Solution Overview
Problem
Existing automated image classification tools, such as neural networks, require significant human intervention for feature selection and are not capable of rapidly and accurately classifying various types of images without pre-deciding features, limiting their applicability in domains like geoscience and industrial applications.
Innovation Solution
A novel image classification algorithm and cognitive analysis system utilizing a convolutional neural network (CNN) that automatically learns and weighs discriminating features from entire images, eliminating the need for human intervention in feature selection and enabling rapid and accurate classification across different domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional neural networks are used for image classification, then classification capability is provided, but human intervention is required for feature selection and the process is not rapid enough
Solution Approach 1:
The system employs deep learning algorithms that automatically perform feature selection and extraction without human intervention. The neural network learns discriminative features directly from raw image data, enabling the system to serve itself in the feature engineering process while maintaining high classification speed through automated processing pipelines.
Solution Approach 2:
The patent replaces manual feature selection processes with automated deep learning-based feature extraction. Instead of scientists manually selecting features, the system uses convolutional neural networks and other deep learning models to automatically learn and extract relevant features from images, substituting human mechanical processes with automated computational systems.
2Measurement precision
If feature pre-selection is performed manually, then classification accuracy can be improved, but the process requires significant human time and effort
Solution Approach 1:
The system performs preliminary automated feature extraction and learning before classification. Deep learning models pre-process images by automatically learning hierarchical features and representations, preparing optimized feature sets in advance without requiring manual intervention during the actual classification process, thus reducing time loss while maintaining accuracy.
Solution Approach 2:
The deep learning system automatically performs feature selection and optimization without human intervention. The neural networks self-adjust weights and learn discriminative features directly from data, enabling the system to serve itself in the feature engineering process while maintaining high classification accuracy through automated learning mechanisms.
3Adaptability or versatility
If existing automated tools are used, then some classification capability is provided, but they cannot rapidly classify various image types without pre-deciding features
Solution Approach 1:
The patent implements a universal deep learning-based classification system that can handle multiple image types (photographs, CT scans, infrared images, X-Ray images, millimeter wave images, thermal images) through the same automated framework. The system uses domain-general deep learning models that adapt to different image types without requiring manual feature pre-selection for each domain, achieving both versatility and high classification rates.
Solution Approach 2:
The system replaces domain-specific manual feature engineering with automated deep learning feature extraction. Instead of scientists manually selecting features for each image type, the system uses neural networks to automatically learn appropriate features from raw data across all domains, substituting human mechanical processes with adaptive computational systems that maintain high productivity across diverse applications.
Data Source
AI summary
A method for training an automated classifier of input images includes: receiving, by a processing device, a convolution neural network (CNN) model; receiving, by the processing device, training images and corresponding classes, each of the corresponding classes being associated with several ones of the training images; preparing, by the processing device, the training images, including separating the training images into a training set of the training images and a testing set of the training images; and training, by the processing device, the CNN model utilizing the training set, the testing set, and the corresponding classes to generate the automated classifier.