Automated Swatch Selection via Likelihood and Dominance Scoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing manual techniques for creating representative image swatches are time-consuming, inconsistent, and require significant human intervention, making them inefficient for large datasets and prone to subjective errors.
Innovation Solution
An automated system that uses a swatch determination engine and likelihood scoring engine to extract and compare image swatches, calculating likelihood scores and dominance values to determine a diverse and representative subset of swatches, leveraging techniques such as salient object detection and deep learning neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual techniques are used to select representative swatches, then accuracy of swatch selection can be maintained through human judgment, but time consumption and resource requirements increase significantly
Solution Approach 1:
The patent replaces the manual mechanical process of visual inspection and selection with an automated computer-based system that uses image processing algorithms, color space transformations, and clustering techniques to automatically identify and select representative swatches from image datasets, eliminating the need for manual human intervention while maintaining selection quality
Solution Approach 2:
The system enables the image data itself to 'select' representative swatches through automated algorithms that analyze color distributions, identify dominant colors, and generate swatches based on mathematical computations of color similarity and diversity, allowing the data to serve itself without external human guidance
2Adaptability or versatility
If manual swatch selection is performed by different individuals, then personal interpretation allows for subjective customization, but consistency and reliability of results deteriorate
Solution Approach 1:
The patent transforms the subjective parameter of human interpretation into objective measurable parameters by converting image data into standardized color spaces (RGB, CMYK, LAB), computing quantitative color statistics, and applying mathematical clustering algorithms that consistently identify representative colors based on measurable properties rather than subjective judgment
Solution Approach 2:
The system creates a universal automated methodology that can consistently process any image dataset regardless of who operates it, using standardized algorithms and color models that produce reproducible results across different users, devices, and sessions, thereby establishing reliability and consistency in swatch selection
3Measurement precision
If manual review of each image is conducted, then thorough analysis of image content is achieved, but productivity and processing speed decrease
Solution Approach 1:
The patent segments the image processing task into distinct automated computational stages including color space conversion, statistical moment calculation, clustering algorithm execution, and swatch generation, allowing each segment to be processed efficiently by specialized algorithms that can operate in parallel on multiple images simultaneously, thereby maintaining analysis quality while dramatically increasing throughput
Data Source
AI summary
Certain embodiments involve determining a diverse and representative subset of swatches of an input image. For example, a graphic manipulation application determine a salient object in an image. The graphic manipulation application extracts multiple swatches from the image. In some cases, the swatches include pixels included in the salient object. The graphic manipulation application computes selection scores for the multiple swatches by combining, for each swatch, a likelihood score indicating a representativeness of a pattern depicted by a respective swatch, and a dominance score indicating a dominance of the pattern depicted by the respective swatch. The graphic manipulation application generates, based on the selection scores of the multiple swatches, a subset of the multiple swatches extracted from the image.