# ABANICCO: A New Color Space for Multi-Label Pixel Classification and Color Analysis
###### Abstract  
Classifying pixels according to color, and segmenting the respective areas, are necessary steps in any computer vision task that involves color images. The gap between human color perception, linguistic color terminology, and digital representation are the main challenges for developing methods that properly classify pixels based on color. To address these challenges, we propose a novel method combining geometric analysis, color theory, fuzzy color theory, and multi-label systems for the automatic classification of pixels into 12 conventional color categories, and the subsequent accurate description of each of the detected colors. This method presents a robust, unsupervised, and unbiased strategy for color naming, based on statistics and color theory. The proposed model, \"ABANICCO\" (AB ANgular Illustrative Classification of COlor), was evaluated through different experiments: its color detection, classification, and naming performance were assessed against the standardized ISCC-NBS color system; its usefulness for image segmentation was tested against state-of-the-art methods. This empirical evaluation provided evidence of ABANICCO's accuracy in color analysis, showing how our proposed model offers a standardized, reliable, and understandable alternative for color naming that is recognizable by both humans and machines. Hence, ABANICCO can serve as a foundation for successfully addressing a myriad of challenges in various areas of computer vision, such as region characterization, histopathology analysis, fire detection, product quality prediction, object description, and hyperspectral imaging. image color analysis; image analysis; semantics; fuzzy color space; color modeling; color segmentation; color classification; human perception +
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problems exist that render the creation of such a perfect color analysis algorithm difficult. The main problem with color vision is that human perception of color is not homogeneous: each person has their own experience of color, influenced by context, culture, education, and lighting [3; 4]. Furthermore, human perception is also influenced by the phenomenon of color constancy, by which our brains perceive color homogeneously on surfaces, despite possible changes of lights or reflections of brighter colors, in contrast to the pixel-level differences in color detected by computers [5]. Thus, it is impossible to create a computer method able to reproduce the infinite possible experiences of color. The second problem, influenced by the differences in human perception of color, is that color names are not universal. The same shade of red could be called \"crimson\" or \"cherry\", depending on the observer [6]; therefore, there is a great disparity between linguistic descriptions of color and computer representations. This problem is known in the literature as 'the semantic gap' [7]. The ultimate challenge for color vision algorithms is that there are no well-defined boundaries between color categories, i.e., there are no immediate methods of finding, for example, the limit between orange and red, or between yellow and green. Humans will, in fact, resort to compound names (reddish-orange, yellowish-green) when confronted with colors located in fuzzy areas between more established terms [8]. Many alternatives have been introduced to deal with these problems, in order to create accurate computer vision methods for color detection and classification. The current research on color analysis primarily focuses on two areas: (1) color naming and detection; (2) color segmentation. Studies on color naming and detection aim to find a mapping between digital color representation and human language for color: this is a complex task, because of the subjective nature of human understanding of color [9].