Lexical Color Classifier System for Categorical Difference Detection
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Solution Overview
Problem
Existing color difference metrics, such as CIELAB, fail to account for categorical color differences, leading to inaccuracies in perceiving large color variations, like those between blue and purple, which are quantified as small differences despite being perceptually distinct.
Innovation Solution
The system transforms initial color attribute values into lexical color classifiers using a machine color naming process, applying algorithms like k-nearest neighbor to categorize colors and calculate cosine similarity measures, allowing for the determination of categorical color differences and similarities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional geometric color difference metrics (e.g., CIELAB) are used to calculate color differences, then computational simplicity is maintained, but categorical color differences are not accurately captured, leading to perceptual inaccuracy
Solution Approach 1:
The patent segments the color comparison process into two distinct parts: (1) geometric color space distance calculation using traditional metrics like CIELAB, and (2) lexical color classifier comparison using machine learning models. This segmentation allows each method to handle different aspects of color perception - geometric methods handle continuous color variations while lexical methods handle categorical boundaries, thereby resolving the contradiction between measurement accuracy and system complexity
Solution Approach 2:
The patent introduces lexical color classifiers as an intermediary layer between traditional color metrics and human perception. These classifiers, generated through machine color naming processes, act as mediators that translate geometric color distances into perceptually meaningful categorical differences. This intermediary enables the system to capture categorical color differences without completely replacing established geometric metrics, balancing accuracy improvement with acceptable system complexity
2Measurement precision
If lexical color classifiers and machine color naming processes are applied to capture categorical color differences, then perceptual accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-generating lexical color classifiers and color naming models before actual color comparison tasks. These pre-computed lexical representations are stored and reused across multiple comparisons, thereby capturing complex categorical color relationships in advance. This approach shifts computational complexity from runtime operations to offline preparation, improving real-time measurement precision while managing overall system complexity
Solution Approach 2:
The patent adds a lexical dimension to traditional geometric color space analysis. Instead of relying solely on continuous numerical color values in spaces like CIELAB, the system incorporates discrete lexical color classifiers that represent categorical color perceptions. This dimensional addition enables the system to detect categorical boundaries that geometric metrics miss, improving measurement precision for perceptual applications
Data Source
AI summary
A system including a memory to store instructions and initial color representations and a processor to execute the instructions in the memory to reference a database having a set of lexical color classifiers, transform each of at least a pair of initial color representations to color values associated with the set of lexical color classifiers, and generate a relative similarity measure related to the at least a pair of initial color representations based on the color values associated with the set of lexical color classifiers.