# Segmenting 20 Types of Pollen Grains for the Cretan Pollen Dataset V1 (CPD-1)
## 5 Results  
The performance of the segmentation pipeline is reported in Table 1. In particular, there are two metrics that are presented, i.e., the number of the detected pollen grains and the method's accuracy for detecting pollen grains for each one of the pollen types. The performance results in Table 1 shows that the proposed method can detect each individual pollen grain with a high accuracy across all types of pollen. In addition, the proposed dataset consists of more than 4000 unique pollen grains from 20 types of plants, making it the second largest publicly available pollen dataset and the largest one regarding pollen that has been collected directly from plants, as seen in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**ID** & **Plant Name** & **Detected** & **Accuracy** & **ID** & **Plant Name** & **Detected** & **Accuracy** \\ \hline
1 & _Thumbra_ & 146 & 98.7\% & 11 & _Pinus_ & 29 & 100\% \\ \hline
2 & _Erica_ & 181 & 97.3\% & 12 & _Calicotome_ & 298 & 94.0\% \\ \hline
3 & _Castanea_ & 218 & 81.0\% & 13 & _Salvia_ & 178 & 96.7\% \\ \hline
4 & _Eucalyptus_ & 170 & 97.1\% & 14 & _Sinapis_ & 197 & 97.5\% \\ \hline
5 & _Myrtus_ & 786 & 79.4\% & 15 & _Ferula_ & 83 & 90.2\% \\ \hline
6 & _Ceratonia_ & 100 & 96.1\% & 16 & _Asphodelus_ & 34 & 100\% \\ \hline
7 & _Urginea_ & 109 & 99.1\% & 17 & _Oxalis_ & 139 & 96.5\% \\ \hline
8 & _Vitis_ & 269 & 95.4\% & 18 & _Pistacia_ & 34 & 100\% \\ \hline
9 & _Origanum_ & 171 & 99.4\% & 19 & _Ebenus_ & 22 & 100\% \\ \hline
10 & _Satureja_ & 71 & 100\% & 20 & _Olea_ & 790 & 92.9\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Method performance results.  
Figure 4: A mosaic of all segmented pollen grains, numbered as in Table 1.  
Regarding the _Myrtus_ pollen grains, which were detected with a low accuracy of 79.4%, there was a dense concentration of pollen grains in the images, with many overlaps and occlusions, as seen in Figure 5. Such overlaps and occlusions make it difficult for the method to properly distinguish each unique pollen grain. A similar problem is observed on the _Castanea_ pollen grains, as seen in Figure 6, on which the detection accuracy was 81%. These particular pollen grains have a very small size as can be seen in Figure 4-3 and Figure 6, compared to the rest pollen types. Thus, their small size combined with possible overlaps, make their detection trickier. However, there are more than 1000 and 200 pollen grains detected for each pollen type, respectively, which can be enough for a classification analysis when their unique shape and characteristics, as well as the number of the detected pollen grains of the rest species are also considered.  
Although there is an imbalance for some pollen types, such as _Pinus_ (Figure 4-11) and _Asplodelus_ (Figure 4-16) their shape and characteristics are unique when compared to the rest of the pollen types. Thus, we do not expect a great performance loss in a machine learning classification task regarding these pollen classes. Similarly, we do not expect much performance issues when the _Ebenus_ (Figure 4-19) pollen is considered, due to its similar size and shape to the _Ferula_ (Figure 4-15) pollen. The former is rather smooth both on its outline and within it, in contrast to the latter, which has a rather harsher outline and texture. On the other hand, the same does not apply for the _Pistacia_ pollen (Figure 4-18), which has a very similar shape and texture with other pollen types, such as _Sinapis_ (Figure 4-14). As a result, we expect that these pollen types will be harder to distinguish from one another. In order to deal with such imbalancing, and increase the data, image augmentation techniques can be used.