Masked Image Scoring Reasoning for Stable Color Impact Analysis
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Solution Overview
Problem
Existing machine learning models struggle to isolate the reasoning behind image appeal patterns and require substantial processing power for explanatory models, leading to unstable explanations and high resource consumption.
Innovation Solution
A method using a mask to identify impactful image areas, generating multiple images with varying colors or attributes within the masked area, and analyzing their performance scores without relying on explanatory models, thereby reducing processing requirements and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If explanatory models (e.g., SHAP, LIME) are used to identify the reasoning behind machine learning model decisions, then interpretability of image scoring is improved, but processing power requirements increase substantially and explanations become unstable
Solution Approach 1:
The patent extracts only the essential visual features (color, shape, texture) that directly influence image appeal scores, rather than using complex explanatory models to analyze all image data. This extraction approach provides interpretability by focusing on key attributes while avoiding the substantial processing power requirements of models like SHAP or LIME.
2Loss of information
If explanatory models are used to provide reasoning behind machine learning predictions, then understanding of image appeal patterns is improved, but the explanations diverge from theoretical properties and become unstable
Solution Approach 1:
The patent segments the image analysis into distinct visual feature categories (color, shape, texture) and evaluates each separately to determine its contribution to image appeal. This segmentation provides stable and interpretable explanations by breaking down the complex prediction into reliable, independent feature assessments rather than using unstable explanatory model outputs.
Data Source
AI summary
A method includes obtaining an image, the image associated with a mask corresponding to a portion of the image, generating a plurality of images based on the image and the mask, each image of the plurality of images depicting a different color in the portion of the image corresponding to the mask, executing a machine learning model to generate an image performance score for each of the plurality of images, ranking the plurality of images according to the image performance scores for the plurality of images, and generating a record comprising one or more images of the plurality of images based on the rankings of the plurality of images.