Image Classification Based on Opacity Fogging Attributes
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Solution Overview
Problem
Service providers face challenges in efficiently analyzing and classifying images based on their fogging attributes due to time and human resource constraints, with existing techniques failing to accurately determine whether an image is normal or afflicted with fog/haze and not incorporating human subjective judgments during quality prediction.
Innovation Solution
A system that automatically classifies images as fogging-afflicted or non-fogging-afflicted by processing image data to extract fogging attributes and mapping them to features of a classifier trained on human expert judgments, using color-based localized contrast features and machine learning algorithms to predict partial or full fog affliction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual image quality assessment is performed by human experts, then measurement precision of fogging attributes is improved, but productivity deteriorates due to time and human resource constraints
Solution Approach 1:
The patent creates a digital copy of human expert knowledge by training a machine learning classifier on human-annotated image data. The classifier learns to replicate human judgment patterns for fogging detection, enabling automated assessment that mimics expert human analysis without requiring actual human experts to perform each evaluation.
Solution Approach 2:
The patent replaces the mechanical system of manual human review with an automated computational system. The machine learning classifier processes images algorithmically, substituting human visual inspection and decision-making with automated feature extraction and classification based on trained models.
2Productivity
If automated classification systems are implemented, then productivity is improved, but measurement precision deteriorates due to lack of human subjective judgment incorporation
Solution Approach 1:
The patent performs preliminary action by collecting and annotating a training dataset with human expert judgments before deploying the automated system. Human experts annotate images with fogging assessments in advance, and these annotations are used to train the classifier, embedding human subjective judgment criteria into the automated system before it begins operational classification.
Solution Approach 2:
The patent implements feedback by using human expert annotations as ground truth to train and evaluate the classifier. The system learns from the feedback provided by human judgments during training, continuously improving its measurement precision by adjusting its parameters to better match human expert assessments.
3Measurement precision
If comprehensive manual review of all images is performed, then measurement precision is improved, but loss of time increases significantly
Solution Approach 1:
The patent creates a computational copy of the manual review process that operates at machine speed. The trained classifier replicates the decision-making logic of manual review without requiring actual human time investment, enabling rapid processing of large image volumes while maintaining assessment quality.
Solution Approach 2:
The patent changes the operational parameters from manual human review speed to automated computational processing speed. By transforming the assessment process into an algorithmic operation, the system achieves orders of magnitude improvement in processing speed while maintaining measurement precision through trained model parameters.
Data Source
AI summary
An approach is provided for automated classification of an image based on the fogging attributes associated with the image. The approach involves processing and/or facilitating a processing of image data associated with at least one image to cause, at least in part, an extraction of one or more fogging attributes from the image data. The approach also involves causing, at least in part, a mapping of the one or more fogging attributes to one or more features of at least one classifier, wherein the at least one classifier is trained based, at least in part, on a co-registration of the one or more features to one or more records of previously made image judgements. The approach further involves causing, at least in part, a classification of the at least one image as either in a fogging-afflicted state or a non-fogging-afflicted state based, at least in part, on the at least one classifier.