Image Evaluation Models Using Neural Feature Weighting and PCA
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Solution Overview
Problem
Existing systems struggle to efficiently identify and categorize images based on similarity and uniqueness, particularly in large datasets, without relying on manual evaluation, and fail to account for nuanced human perception of image features.
Innovation Solution
A system and method for image evaluation using machine learning techniques to extract features from images, construct generative models, and apply regression algorithms to determine similarity and uniqueness scores, reducing dimensionality through methods like PCA, and utilizing neural networks for object and scene detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual evaluation methods are used to identify and categorize images, then accuracy in understanding human perception of image features is improved, but productivity and efficiency deteriorate due to the time-consuming nature of manual processes
Solution Approach 1:
The system enables automated image evaluation where the computational model independently performs feature extraction, similarity detection, and categorization without requiring manual human intervention for each image evaluation task
Solution Approach 2:
The patent replaces manual human evaluation processes with an automated computational system using machine learning models, neural networks, and regression algorithms to perform image feature analysis and similarity detection
2Measurement precision
If traditional image comparison methods are used, then simplicity of the system is maintained, but measurement precision and accuracy in detecting image similarity deteriorate
Solution Approach 1:
The system segments the image evaluation process into distinct functional modules: feature extraction module, dimensionality reduction module, generative model construction module, and similarity detection module, allowing each to be optimized independently while maintaining overall system accuracy
Solution Approach 2:
The patent transforms image data from pixel space to feature space through neural network extraction and further reduces dimensions using PCA, creating a compressed representation that captures essential similarity characteristics while reducing computational complexity
3Loss of information
If high-dimensional feature representations are used, then completeness of image feature information is improved, but computational intensity and processing time worsen
Solution Approach 1:
The system extracts only the most relevant and discriminative features from images using neural networks, eliminating redundant pixel-level data while preserving essential semantic information needed for similarity detection
Solution Approach 2:
The patent applies dimensionality reduction techniques including PCA to transform high-dimensional feature vectors into lower-dimensional representations that retain the most significant variance and information content, reducing computational load while maintaining accuracy
Data Source
AI summary
A method may include executing a neural network to extract a first plurality of features from a plurality of first training images and a second plurality of features from a second training image; generating a model comprising a first image performance score for each of the plurality of first training images and a feature weight for each feature, the feature weight for each feature of the first plurality of features calculated based on an impact of a variation in the feature on first image performance scores of the plurality of first training images; training the model by adjusting the impact of a variation of each of a first set of features that correspond to the second plurality of features; executing the model using a third set of features from a candidate image to generate a candidate image performance score; and generating a record identifying the candidate image performance score.