Color Selection for Retail Item Variants
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Solution Overview
Problem
Existing systems fail to effectively select and represent multiple colors in images of retail item variants, particularly when multiple colors are present, leading to inaccurate representation in sample images.
Innovation Solution
The system identifies dominant colors in images of child variants by categorizing colors into subgroups, selecting representative colors, and generating color value distribution representations, allowing for accurate and efficient selection of colors for sample images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional color selection methods are used for retail item variants, then the process is simple, but the accuracy of color representation in sample images deteriorates when multiple colors are present
Solution Approach 1:
The patent segments the color selection process into distinct stages: image acquisition, color extraction, color clustering, and representative color selection. By dividing the complex task of selecting accurate colors from multi-colored retail items into manageable segments, the system achieves high color representation accuracy without overwhelming complexity at any single stage.
Solution Approach 2:
The patent introduces color clustering as an intermediary step between raw color extraction and final color selection. This intermediary process groups similar colors and identifies dominant color patterns, serving as a bridge that transforms complex multi-color data into manageable color representations while maintaining accuracy.
2Measurement precision
If manual color selection is performed for each retail item variant, then color accuracy can be ensured, but the time and labor required increases significantly
Solution Approach 1:
The patent implements an automated color selection system that performs color extraction, clustering, and representative color identification without human intervention. The system serves itself by processing retail item images through algorithms that automatically determine dominant colors and generate accurate color representations, eliminating manual labor while maintaining high accuracy.
Solution Approach 2:
The patent replaces the manual mechanical process of color selection with an automated computational system. Image processing algorithms and color analysis software substitute for human eyes and hands, extracting and analyzing color data from retail item images automatically, thereby achieving both accuracy and efficiency.
3Loss of information
If all colors in a multi-colored retail item image are displayed, then complete color information is provided, but the sample image becomes cluttered and difficult to interpret
Solution Approach 1:
The patent extracts only the dominant and representative colors from multi-colored retail item images, separating these key color information from the complete set of all colors present. By taking out only the essential color data that defines the item's appearance, the system maintains color information completeness while eliminating clutter from less significant colors.
Solution Approach 2:
The patent applies different quality levels to different colors based on their importance. Representative colors are given prominence and detailed representation, while less significant colors are either grouped or omitted. This local quality approach ensures that the most important color information is preserved and easily interpretable, while maintaining overall color accuracy.
Data Source
AI summary
Systems, methods, and computer-readable media are disclosed for identifying a set of images of child variants of a parent item, identifying a set of colors in the set of images, categorizing the set of colors into color subgroups, identifying a set of representative color(s) for the color subgroups, generating a color value distribution representation for an image of a child variant that indicates a respective number of pixels in the child variant image corresponding to each of one or more representative colors, identifying a color cluster in the color value distribution representation, scoring the color cluster, and selecting a particular color from the color cluster for inclusion in a sample image of the child variant if the color cluster score meets or exceeds a threshold value.