Color Aesthetics: Fuzzy based User-driven Method for Harmony and Preference Prediction

TL;DR

This paper introduces a fuzzy-based, user-driven method for predicting color harmony and preferences, accounting for individual differences and enabling better aesthetic evaluations in applications like fashion coordination.

Contribution

It presents a novel quantitative approach that predicts color preferences and harmony by combining fuzzy similarity algorithms with user-specific data, accommodating complex color groups.

Findings

Effective prediction of color harmony and preferences.

Ability to process multiple colors beyond pairs.

Personalized aesthetic evaluation results.

Abstract

Color is the most important intrinsic sensory feature that has a powerful impact on product sales. Color is even responsible for raising the aesthetic senses in our brains. Account for individual differences is crucial in color aesthetics. It requires user-driven mechanisms for various e-commerce applications. We propose a method for quantitative evaluation of all types of perceptual responses to color(s): distinct color preference, color harmony, and color combination preference. Preference for color schemes can be predicted by combining preferences for the basic colors and ratings of color harmony. Harmonious pallets are extracted from big data set using comparison algorithms based on fuzzy similarity and grouping. The proposed model results in useful predictions of harmony and preference of multicolored images. For example, in the context of apparel coordination, it allows predicting a preference for a look based on clothing colors. Our approach differs from standard aesthetic models, since in accounts for a personal variation. In addition, it can process not only lower-order color pairs, but also groups of several colors.