# ABANICCO: A New Color Space for Multi-Label Pixel Classification and Color Analysis
## 6 Results and Discussion
### Discussion  
In Section 6, we conducted an empirical evaluation to assess the color naming, classification, and segmentation performance of our proposed method, ABANICCO, compared to different state-of-the-art methodologies. The reported results show that ABANICCO outperforms the benchmark methods in accuracy and simplicity. Specifically, the performed experiments can be divided into two different categories: the first category dealt with color classification and naming, while the second dealt with color segmentation. Firstly, we proved that ABANICCO was able to correctly classify the colors on the official ISCC-NBS color classification Level 2. Moreover, for each color, ABANICCO provided a coherent name that was understandable for humans and machines alike, thus solving the inconsistency between human semantics and machine-extracted features--that is, the semantic gap. Secondly, regarding color segmentation, three additional experiments compared ABANICCO's capabilities with state-of-the-art methods: these experiments showed that ABANICCO is the only method that prioritizes color analysis over area or object segmentation. Moreover, the proposed AB polar description of the colors allows the user to create clusters that solve image segmentation tasks easily. With this simple step of class remodeling, our segmentation pipeline achieved improved segmentation performance compared to state-of-the-art methods for object and area segmentation. Thus, ABANICCO successfully dealt with the common pitfalls of other color analysis approaches. By basing our model on objective color theory, we avoided the danger of subjectivity, inability to handle variations in lighting and noise, and the lack of standardization of other models. Moreover, we ensured the maximum range of detectable colors, by building ABANICCO from CIELAB color space. Finally, ABANICCO is the least complex model compared to the state of the art. This, together with not needing any annotations, makes ABANICCO practical for a wide range of applications, and accessible to researchers, regardless of their field. The experiments conducted suggest that ABANICCO could be useful in scenarios that involve changing objects, an unknown number of classes, limited data, unclear boundaries, or when the user lacks prior knowledge of the image or color theory. Moreover, its robustness and unsupervised nature make ABANICCO particularly useful in cases where deep learning techniques are unavailable, inefficient, or when labeled data is scarce or human-biased. The non-supervised character of the proposed method also eliminates the risk of human perception and context-driven errors in color analysis. Furthermore,  
Figure 7: Segmentation of sample image BONCH-01 of the ITTP-Close experimental dataset, using class remodeling of ABANICCO’s detected colors as clustering-based image segmentation. ABANICCO's potential extends beyond standalone use, and could serve as an initial step in more complex deep learning tasks, such as reducing training time, and improving results in a wide range of tasks, like the introduction of semantic information as organized color data [52; 53], sampling and registration [54], compression [55], image enhancement and dehazing [56; 57], spectral unmixing [58], and soft-labeling [59]. In addition to ABANICCO's proven success in automatic color classification, naming, and segmentation, its efficiency is highlighted by its computational simplicity, allowing it to run quickly even on low-end computers. Our experiments show that ABANICCO was able to analyze the 380 \(\times\) 570 pixels image in Figure 4 in less than a second on a mid-end computer (an HP laptop with i5, 2.4 Hz, and 16 Gb RAM). While the proposed method has many strengths, it does have a drawback that we aim to address in future work: the pipeline for image segmentation currently requires manual intervention by the user, specifically in the class remodeling step. Our future work will explore integrating various automatic clustering methods, to automate the process entirely.