## 4 Experimental Validation
### Methods and Terminology
#### 4.2.1 Pollen Grain Microscopy  
The manual annotation of microscopy pollen grain images is implemented using Grabcut [47], which is both efficient and effective given the simplicity of the image content. Those manually labeled images are then used to train a random ferns model [48], which in turn is used to assign pseudo-labels to the unlabeled images. In practice, we utilize 100 ferns and each fern consists of 10 tests. The point neighborhood is typically defined using a small square window. This square window has a size of \((2r+1)\) by \((2r+1)\) pixels and is centered around the pixel of interest. Within this window, binary tests are performed. Five of them compare the absolute difference between two pixels with t, where t is derived from the statistical distribution of the absolute differences between any two pixels in the 800 by 800 image, and t is randomly selected from the interval \((20,40)\). This choice is motivated by the observation that the majority of difference values fall within the range of 0 to 20.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**(Semi\_)Manual Annotation**} & **Pseudo-Label Generator** & **Test Model** \\ \hline \hline \multirow{4}{*}{Manual Delineation} & X & CNN \\  & X & Random Ferns \\ \cline{1-1}  & Random Ferns \(\tau\) & CNN*\(\_\) & \\ \cline{1-1}  & CNN & CNN*\(\_\)CNN \\ \hline \hline \end{tabular}
\end{table}
Table 4: Models considered on pollen grain microscopy image dataset. See Section 4.2 for a description of the models associated to the terminology. X indicates that the test model has been trained on manually labeled data only, without pseudo-labeled data exploitation.  
Consequently, when two pixels exhibit difference values within this range, they are either both part of the foreground or both part of the background. Therefore, these five tests are capable of distinguishing edges from non-edges. The other five tests involve comparing pixel value with the difference between the peak and the variance of the histogram of the 800 by 800 image. Given that most of the image area represents the background, the peak of the histogram is indicative of the background location. As the pixel values of the background exhibit slight variations, we utilize both the peak and the variance of the histogram to characterize the background. Consequently, these five tests effectively distinguish between the background and foreground.  
The last column in Table 4 presents the test models, i.e., the models whose performance are measured on the test set and reported in our experimental section. The CNN and random ferns models are trained on manually labeled data only, while the CNN* models refer to data-reinforced models, meaning that they are trained on the entire training set based on pseudo-labels derived either from the random ferns or the manually trained CNN. When pseudo-labels are predicted by the CNN, the data-reinforced model is denoted CNN*_CNN. When deriving pseudo-labels from the random ferns, the pseudo-labels are generated either by thresholding the probability map predicted by random ferns at a fixed threshold \(\tau\), leading to a trained convolutional model that is denoted CNN*_\(\tau\), or by considering a uniform distribution of thresholds in an interval \((\tau_{1},\tau_{2})\) to randomly select the threshold each time an image is considered along the SGD training iterations (see Section 3.2). This last definition of pseudo-labels results in a trained data-reinforced model denoted as CNN*_(\(\tau_{1},\tau_{2})\)