Digital Image Aesthetic Scoring From User Interaction Data
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Solution Overview
Problem
Conventional techniques for quantifying visual aesthetics of digital images are subjective, computationally and financially expensive, inaccurate due to biases, and fail in real-world scenarios, often favoring certain types of content over others.
Innovation Solution
A machine-learning model trained with training data including digital images and user interaction data to generate an aesthetic score, utilizing a learning signal extraction module, aesthetics classification module, and self-training module to reduce noise and bias, enhancing accuracy and diversity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional techniques are used to quantify visual aesthetics, then computational resources and financial costs are consumed, but accuracy is reduced due to biases and subjectivity
Solution Approach 1:
The patent replaces complex conventional aesthetic evaluation systems with a machine learning model that automatically processes images. The model substitutes manual or computationally intensive conventional techniques with an automated neural network-based system that reduces both computational overhead and financial costs while maintaining or improving accuracy.
Solution Approach 2:
The patent transforms the aesthetic evaluation process by changing the parameters from subjective human judgment or complex conventional algorithms to quantitative machine learning predictions. The system uses trained models with specific architectural parameters to objectively evaluate aesthetic quality, reducing bias and improving consistency.
2Reliability
If conventional aesthetic evaluation techniques are implemented, then processing can be performed, but reliability is reduced due to biases in user interaction data
Solution Approach 1:
The patent applies preliminary actions by pre-processing user interaction data to detect and correct biases before training the machine learning model. The system performs bias detection and mitigation in advance during the data preparation phase, ensuring that the training data is balanced and representative before model training begins.
Solution Approach 2:
The machine learning model performs self-correction by automatically adjusting for biases in the training data through its learning process. The system uses techniques such as re-sampling, re-weighting, or adversarial debiasing that allow the model to self-correct biased patterns without requiring external intervention during deployment.
3Adaptability or versatility
If conventional techniques are used, then aesthetic evaluation can be performed, but adaptability is reduced due to failure in real-world scenarios
Solution Approach 1:
The patent implements a dynamic machine learning model that can adapt to different types of images and real-world scenarios. The system uses flexible architectural designs that allow the model to learn from diverse data distributions and adjust its predictions based on the specific characteristics of input images, improving both adaptability and accuracy.
Solution Approach 2:
The machine learning model is designed with universal applicability to handle various image types, styles, and contexts. The system uses multi-functional architecture that can evaluate different aesthetic aspects (composition, color, lighting, etc.) simultaneously, making it versatile across diverse real-world applications while maintaining consistent accuracy.
Data Source
AI summary
Digital image visual aesthetic score generation techniques are described. In one or more examples, these techniques are implemented by a system including a training data collection module implemented by a processing device to collect training data including training digital images and user interaction data describing user interaction with the training digital images, respectively. A training module is configured to train a machine-learning model using the training data to generate an aesthetic score based on an input digital image. The aesthetic score is configured to specify an amount of visual aesthetics exhibited by the input digital image.