Color Palette Generation from Images Using Popularity Feedback
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Solution Overview
Problem
Current technologies face challenges in efficiently generating color palettes and names from images, particularly in commerce networks, where accurate color representation is crucial for sales and product matching, but existing methods lack effectiveness in standardization, automation, and user preference integration.
Innovation Solution
An image processing service that generates color palettes by pre-processing images, identifying representative colors, and determining palettes based on user-defined or automated criteria, incorporating color popularity and metadata, while also providing color name generation and validation to ensure accurate color representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated color palette generation is implemented, then productivity is improved, but measurement precision may worsen due to lack of human judgment
Solution Approach 1:
The system incorporates user feedback mechanisms where users can rate, correct, or provide feedback on generated color palettes. This feedback is used to refine and improve the automated color extraction algorithms, allowing the system to learn from human judgment while maintaining high productivity. The feedback loop enables continuous improvement of measurement precision without sacrificing automation efficiency.
Solution Approach 2:
The patent introduces an intermediary layer between automated color extraction and final color palette selection. This intermediary includes validation rules, color theory constraints, and hybrid algorithms that combine automated extraction with predefined color harmony principles. This intermediary layer ensures that automated generation maintains both efficiency and color representation accuracy by filtering and refining raw extracted colors through multiple processing stages.
2Adaptability or versatility
If multiple color processing functions are integrated, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system implements a universal color processing engine that handles multiple functions including color extraction, palette generation, validation, and conversion through a single integrated architecture. This multi-functional core reduces overall system complexity by eliminating the need for separate dedicated systems for each color processing task, while maintaining high adaptability to different input types and output requirements.
Solution Approach 2:
The patent divides the color processing system into modular functional components that can be independently configured and combined. These segments include image processing modules, color space conversion modules, palette generation modules, and validation modules. This segmentation allows the system to adapt to different requirements by activating only necessary modules, reducing effective complexity while maintaining versatility.
3Reliability
If user preferences are incorporated, then reliability is improved, but loss of time increases due to additional processing
Solution Approach 1:
The system performs preliminary processing of user preferences and constraints before actual color palette generation. User preferences are pre-validated, pre-processed into actionable parameters, and pre-integrated with color theory rules during the setup phase. This preliminary action ensures that when color palettes are generated, the reliability improvements from user preferences are achieved without adding significant processing time during the actual generation phase, as the preference processing is done in advance.
Data Source
AI summary
Systems and methods are provided for generating color names for colors corresponding to images and/or palettes. A color image is obtained, and one or more color palettes corresponding to the color image are identified. The color palette may be generated based on palette generation criteria, which may facilitate or control a palette generation process. Illustratively, the palette generation process may include image pre-processing, color distribution generation, representative color identification, palette candidate generation, and palette determination. A color name for each color identified in the color palette and/or the color image can be identified based at least in part on color name popularity information. Color name popularity information may be identified from color name-related voting results provided by a social network site. Aspects of the disclosure are further directed to processing the identified color name(s), such as updating color name metadata associated with the original color image and/or the color palette.