# ABANICCO: A New Color Space for Multi-Label Pixel Classification and Color Analysis
## 6 Results and Discussion
### Segmentation
#### 6.2.1 Comparison with Fuzzy Color Spaces--Discrete Colors  
For this second experiment, we compared ABANICCO with the most representative method of the state of the art for color classification. In their work, [PERSON] et al. (2018) used Fuzzy Logic and space optimization to obtain a suitable set of prototypes upon which to build a fuzzy color space for each image or set of similar images. In contrast to ABANICCO, their method required annotations, and did not detect individual colors. Furthermore, in some cases, the user had to decide the number of categories for prototype learning. Their method returned, as output, a map of the membership degree of each pixel to the studied category, which they represented by modifying the rate of transparency of the segmented images. Specifically, they successfully segmented images of country flags into classes corresponding to each flag's original colors. Compared to a human-generated ground truth of 12,000 color samples, they obtained a 0.955 f-score. The ground truth was not created as a complete segmentation of all the areas in the images, but rather as small, 10-pixel samples of consistent colors within the images: this approach prevented the possible variations of the expected colors from being included in the ground truth. Thus, their method was tested against a partial and biased truth. In this section, we quantitatively compared the segmentation given by ABANICCO directly to their results, as opposed to using their ground truth, as it is not publicly available. The assessment of segmentation performance is most often determined through the use of quantitative metrics, such as object overlap and distance analysis: however, we argue that such approaches would fail to fully capture the nuances inherent in the membership maps obtained in [41]. For lack of an adequate, problem-aware, quantitative metric that could compare such different outputs [50], we preferred to assess the differences in performance quantitatively in Figure 4.  
We compared the performance of both methods, using the image of the United States of America flag seen in Figure 4A. The image selected presented clear boundaries between colors. Moreover, the colors were arranged in clusters far away from each other in the color wheel, which can be appreciated in the polar description of the image in Figure 4G: this image is a prime example of how lighting and movement can affect the colors of an object, creating browns on the shaded areas of the red stripes, and new tones of blue as a result of dark-blue areas reflecting over the white stripes and stars. Nevertheless, the phenomenon of color constancy, human bias, and pre-existing notions about the flag will lead most observers to detect only three colors. These variations of the expected colors are marked in Figure 4, rows E and D.  
As ABANICCO is designed for color segmentation and classification, its initial segmentation may not align with the ground truth, as it prioritizes accurate color recognition over object segmentation. In this case, it was clear that the segmentation errors were actually a result of ABANICCO's focus on legitimate color classification: the areas in which either blue or red were reflected onto the white stripes were classified as blue and red, their true colors. The actual color of these areas can be seen marked with an orange and green star, respectively, in the full colors summary of Figure 4E. Therefore, the segmentation using ABANICCO was accurate in terms of color but not in terms of object edges or expected human segmentation. To achieve the expected segmentation, we used the information provided by the shade classification (Figure 4E ) and polar description (Figure 4G) to easily modify the class boundaries and classes, so as to best match the application (Figure 4F): this simple step of boundary remodeling allowed us to obtain the expected (biased) segmentation for this application, by controlled, informed decisions based on color theory.