Neural Network Color Theme Generation
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Solution Overview
Problem
Existing techniques for generating multiple color theme variations from an input image are cumbersome, requiring significant designer input and often relying on reference images, which limits the ability to create direct variations.
Innovation Solution
A machine learning-based system that uses a neural network to model color distributions, allowing for the prediction of new color themes from an input image without the need for a reference image, and includes a color theme evaluation network to ensure aesthetic quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual recoloring is performed by designers, then color variations can be created with aesthetic quality, but the process is tedious and time-consuming
Solution Approach 1:
The patent replaces the manual mechanical process of designer recoloring with an automated neural network system. The neural network takes an input image and automatically generates multiple color theme variations without human intervention, substituting the mechanical manual coloring process with an automated deep learning-based system that produces aesthetically pleasing results in seconds
Solution Approach 2:
The system enables self-service by allowing users to input an image and automatically receiving multiple color variations without requiring designer expertise or manual input. The neural network performs the recoloring task autonomously, making the service accessible to anyone without professional design skills
2Productivity
If prior techniques are used to generate color variations, then multiple variations can be created, but significant designer input and reference images are required
Solution Approach 1:
The patent extracts and eliminates the requirement for reference images and significant designer input from the color variation generation process. The neural network is trained to generate variations directly from the input image itself, removing the need for additional reference materials or manual guidance that were necessary in prior techniques
Solution Approach 2:
The neural network serves multiple functions: it analyzes the input image, generates multiple color theme variations, and can do so without requiring separate reference images for each variation. This multi-functional approach consolidates what previously required multiple separate inputs and processes into a single operation
3Reliability
If existing recoloring techniques are applied, then color transfer can be achieved, but the system complexity increases due to multiple networks and iterative processes
Solution Approach 1:
The patent segments the recoloring process into distinct functional components: a neural network for generating color theme variations and a separate evaluation network for assessing aesthetic quality. This segmentation allows each component to be optimized independently while working together to produce reliable results, dividing the complex task into manageable functional blocks
Data Source
AI summary
Embodiments are disclosed for generating multiple color theme variations from an input image using learned color distributions. A method of generating multiple color theme variations from an input image using learned color distributions includes obtaining, by a user interface manager, an input image, determining, by a color extraction manager, one or more color priors based on the input image, generating, by a color distribution modeling network, a plurality of color theme variations based on the one or more color priors, ranking, by a color theme evaluation network, the plurality of color theme variations, and generating, by a recolor manager, a plurality of recolored output images using the plurality of color theme variations.