# ABANICCO: A New Color Space for Multi-Label Pixel Classification and Color Analysis
## 3 Related Work  
Color analysis poses numerous challenges, including variations in perception, limited linguistic mapping between language and machines, and lack of clear boundaries, as explained in the previous section. Currently, color analysis research primarily focuses on resolving two related problems: (1) color naming and detection; and (2) color segmentation. Much research has been devoted to color naming and detection, with the aim of finding a suitable mapping between the digital representation of colors and the language employed by humans when referring to them to bridge the semantic gap [6; 9; 36]. However, this is a complex task due to the subjective nature of human understanding of color, which can vary, based on factors like age, context, profession, and eye morphology. The only way to truthfully close the semantic gap would be to run a multitudinary experiment with a sufficiently heterogeneous cohort that would consider every possible experience of color: however, this would be highly impractical. Fuzzy models, which tackle the notoriously ill-defined boundaries between colors, are being used as a practical alternative. Fuzzy set theory is closer to the human cognitive process of color categorization than crisp color partitions [7]: because of this, several research works have applied fuzzy logic to successfully obtaining a correspondence between human perception, color language, and computational color representation. Within this approach, three subgroups of methods stand out: (1) those that rely on geometrically regular membership functions; [2; 37; 38]; (2) those that formalize fuzzy color spaces from human-obtained prototypes [6; 36]; and (3) those that create adapted fuzzy color spaces dependent on application [8; 39; 40; 41; 42]. Methods in the first subgroup are problematic, becausethey rely on color spaces with arbitrary distances between color categories. Moreover, using regular, homogeneous functions disregards the irregular form of color terms [43]. Methods in the second subgroup provide good color models, but depend on the number of study participants, and focus on a limited number of color terms obtained from subsets of a single cultural environment (like Berlin and Kay). Methods in the third subgroup favorably address the limitations of the previous methods, and achieve good results in color segmentation, as well as in color detection and naming: however, the resulting pipelines are complex and inaccessible for regular use, and are challenging to adapt to new applications. In addition to fuzzy color models, color quantization is widely used for color segmentation. Color space quantization is based on clustering, usually by nearest neighbor classification or color thresholding. Classification based on nearest neighbors finds the \(k\) closest neighbors to any given pixel in a set of predefined color classes [10; 11]: this approach is limited by the necessary definition of the clusters and boundaries, which depends entirely on the training set, although some recent research provides more robust results [44]. Alternatively, color thresholding divides the space into segments defined by mostly-linear boundaries that need to be manually decided: this step has been solved using machine learning techniques, which require a priori knowledge of the labels of the color categories and training process, or by complex deep learning optimization algorithms [45; 46; 47]. Like the previous method, this approach is severely limited by the dependence on training data [10; 11]. Nevertheless, the problem most previous methods have in common is disregarding color theory fundamentals. Excepting the most recent fuzzy model approaches, most of the above-mentioned approaches are based on misinformed notions of what colors are: these inaccuracies are often due to the confusion between pure hues and color terms. Basic Color Terms (BCTs, as introduced by Sturges & Whitfield in 1991) include 11 categories: Black; White; Red; Green; Yellow; Blue; Brown; Orange; Pink; Purple; and Gray [27; 43].