# ABANICCO: A New Color Space for Multi-Label Pixel Classification and Color Analysis
## 6 Results and Discussion
### Segmentation
#### 6.2.3 Object Segmentation--ITTP-Close Dataset  
In this final experiment, we present a quantitative analysis of ABANICCO'S performance when applied to the problem of object segmentation. To this end, the technique of class remodeling described in Section 6.2.1 was tested on natural images of the ITTP-Close dataset introduced in [51]: this dataset consisted of five images (450 \(\times\) 500 pixels) of scenes with a close light source. All the images contained volumetric objects, some of which included highlights. The annotations for the ground truth were done by splitting the images into small subregions with guaranteed color constancy, and manually merging them when the colors were indistinguishable by the human eye.  
Figure 6 shows the images in the dataset, and their corresponding ground truth; moreover, it presents an excellent example of why image segmentation based on color as an attribute is not the same as color segmentation. In Figure 6C, we see that in a singular area of the image, as defined by the ground truth, there was a wide range of distinct colors that even differed in hue: for example, the book appeared as mostly uniform, unsaturated greenish blue; however, it also showed pinkish browns from the reflection of the orange piece, and yellowish greens from the reflection of the yellow cylinder. These variations were not considered in the ground truth, as they would have interfered with object segmentation, which shows why using color for image segmentation is not the same as color segmentation.  
In Figure 7, we see the polar description of this same image, as provided by ABANICCO. It is interesting to observe that in our polar AB color space, the different colors of an area appear naturally grouped in defined clusters; therefore, by simply moving the class boundaries, we obtain a perfect segmentation of the objects present in the image.  
In this experiment, we were testing segmentation of objects with clear boundaries, which were easier to annotate than the previous cases explored in this work. Thus, we could easily test this segmentation pipeline against the method proposed by the authors of the dataset in [51]. In their work, they proposed a region adjacency graph framework with color space projective transform as a pre-processing technique. This method is not automatic, and requires initialization. In their work, the authors used the dataset median Intersection over Union score (dataset-mIoU) for the evaluation of their segmentation, as defined by  
\[\text{dataset-mIoU }=\sum_{k=0}^{K}\min\bigl{(}\text{IoU}\bigl{(}S_{k}^{*}, \bar{S}_{k}\bigr{)},0.5\bigr{)}, \tag{5}\]  
where \(K\) was a number of ground truth segments. The quality range was also in \([0,1]\).  
The authors of [51] reported a dataset-mIoU of 0.85 for their method. By contrast, our segmentation pipeline achieved a dataset-mIoU score of 0.96, representing a 13% improvement.  
Figure 6: The ITTP-Close experimental dataset: (**A**) RGB images present in the dataset; (**B**) the corresponding groundtruths; (**C**) a closer look at the different colors in one area, as defined by the ground truth.