## 4 Experiment and Results
### Novelty/Noise Detection during Online Learning  
This section has discussed the results of the PKELM novelty detection mechanism for detecting the noising samples and for correctly assigning labels to unlabeled data instances during online learning. Figure 22 shows the samples images of non-food images in the dataset. We presented a well-trained model on the feature vectors from the UECFOOD100 dataset with 200 training instances. The data contains noisy samples. It consists of 150 feature vectors of food images and 50 feature vectors of non-food images. Figure 23 shows that the novelty detection mechanism correctly labels 131 feature vectors of food images as non-noisy samples. It wrongly labels 19 feature vectors of food images as noisy samples. Similarly, it correctly classifies 43 out of 50 feature vectors of non-food images as noisy instances before updating the model.  
Figure 21: Results of ingredient detection on randomly selected images from the VireoFood-172 dataset.  
Figure 23: Activation values on network trained with UECFOOD100 dataset. Blue dots represent non-noisy food images. Orange dots represent food images classified as noisy samples. Grey dots show non-food images correctly determined as noisy samples. Yellow dots represent non-food images wrongly classified as non-noisy samples.  
\begin{table}
\begin{tabular}{l c c c c c} \hline
**Datasets** & \(\Omega_{base}\) & \(\Omega_{all}\) & \(\Omega_{new}\) & \(I_{k}\) & \(F_{k}\) \\ \hline
**PFID-Baseline** & & & & & \\ CIELM & 0.3532 & 0.5441 & 0.5932 & 0.2532 & 0.1306 \\ ACIELM & 0.0022 & 0.1445 & 0.8234 & 0.8279 & 0.8359 \\ ARCIKELM & 0.0395 & 0.6618 & 0.7570 & 0.1794 & 0.0889 \\ PKELM (RBF) & 0.5391 & 0.8067 & 0.7860 & 0.1002 & 0.0986 \\ PKELM (chi-squared) & 0.5391 & 0.8074 & 0.7901 & 0.0950 & 0.0972 \\
**Pakistani Food** & & & & & \\ CIELM & 0.7905 & 0.7330 & 0.7492 & 0.0663 & 0.1044 \\ ACIELM & 0.4026 & 0.4605 & 0.9482 & 0.5481 & 0.5399 \\ ARCIKELM & 0.8044 & 0.7969 & 0.8289 & 0.0240 & 0.0848 \\ PKELM (RBF) & 0.7860 & 0.8253 & 0.8670 & \(-\)0.0169 & 0.0943 \\ PKELM (chi-squared) & 0.8863 & 0.8409 & 0.8646 & \(-\)0.0158 & 0.0934 \\
**Food101** & & & & & \\ CIELM & 0.9069 & 0.8654 & 0.8695 & 0.0512 & 0.0575 \\ ACIELM & 0.4677 & 0.5688 & 0.9897 & 0.4642 & 0.5861 \\ ARCIKELM & 0.9430 & 0.9055 & 0.9078 & 0.0051 & 0.0417 \\ PKELM (RBF) & 0.9335 & 0.9042 & 0.9109 & 0.0056 & 0.0473 \\ PKELM (chi-squared) & 0.9412 & 0.9168 & 0.9244 & \(-\)0.0332 & 0.0620 \\ UECFOOD100 & & & & & \\ CIELM & 0.9500 & 0.9123 & 0.8319 & 0.0410 & 0.0688 \\ ACIELM & 0.3714 & 0.5450 & 0.9759 & 0.5240 & 0.6520 \\ ARCIKELM & 0.9642 & 0.9254 & 0.8651 & 0.0128 & 0.0512 \\ PKELM (RBF) & 0.9533 & 0.9363 & 0.8910 & \(-\)0.0005 & 0.0755 \\ PKELM (chi-squared) & 0.9557 & 0.9379 & 0.8861 & \(-\)0.0063 & 0.0563 \\
**UECFOOD256** & & & & & \\ CIELM & 0.5362 & 0.7227 & 0.7757 & 0.1059 & 0.1236 \\ ACIELM & 0.1006 & 0.4722 & 0.9482 & 0.5481 & 0.5399 \\ ARCIKELM & 0.8808 & 0.8454 & 0.8691 & \(-\)0.0049 & 0.0839 \\ PKELM (RBF) & 0.8880 & 0.8491 & 0.8647 & \(-\)0.0332 & 0.0755 \\ PKELM (chi-squared) & 0.8109 & 0.8495 & 0.8810 & \(-\)0.0106 & 0.0876 \\ \hline \end{tabular}
\end{table}
Table 7: Comparison of average catastrophic forgetting measures during progressive learning for food category classification on each food dataset.  
Figure 22: Samples of non-food images used in our experiments.  
The second experiment took 160 feature vectors of unlabeled feature vectors of food images. The objective is to assign labels based on the PKELM novelty detection mechanism during online learning. The classifier trained on the UECFOOD256 dataset has a mean threshold of 0.11031 and a standard deviation of 0.0985 on the training dataset. Figure 24 shows it assigns correct labels to 82 feature vectors of food images and mislabels two data inputs. It classified the remaining data instances as noisy samples, which means that the model does not have sufficient information to label them correctly.