# Effect of Camera Choice on Image-Classification Inference
## 4 Results and Discussion
### Scenario 3  
Scenario 3 involves the most complex background of the three scenarios (see Figure 6). Figures 18 and 19 illustrate stacked bar charts showing the proportion of images classified as a cup or a coffee mug by the pretrained Resnet-18 model as a function of distance for different combinations of drinking vessel and camera. In Figure 18, only the top prediction class of an image is considered; in Figure 19, the top five prediction classes are considered such that if a cup and/or coffee mug appears in the top five, the highest ranking of these two classes is selected. The following subplots of Figures 18 and 19 are shaded with a gray face color to designate that they are of interest with respect to demonstrating a dependence of the classification on camera:  
* Microsoft LifeCam and Vessel 2: the image classifier shows a propensity for classification of the vessel as a coffee mug, whereas with other cameras, the same classifier is more likely to select a cup.
* Angetube 962A and Vessel 5: the image classifier shows a propensity for classification of the vessel as a cup, whereas with other cameras, the same classifier is more likely to select a coffee mug.  
In fact, these observed differences in the classification output are more pronounced than those observed in Scenario 2 and demonstrate a very significant effect of the camera used on the image classifier. Figure 20 compares the selectivity of the pretrained Resnet-18 model as a function of distance for the Angetube962, Logitech C920e, and Microsoft Lifecam. It is apparent that most of the selectivity values are very small for Vessel 2 and Vessel 5 in Scenario 3, which was not the case in Scenario 1 or Scenario 2. Again, this may explain the differences in classification by the image classifier highlighted above for these vessels. Specifically, the low selectivity means the image classifier is not easily able  
Figure 17: Selectivity \(\sigma\) (i.e., the mean absolute difference between prediction probabilities for cup and mug classes) comparison for cameras with similar FOV (scenario: Scenario 2).  
to distinguish between a cup and a coffee mug, so any small difference in the images produced by different cameras may be sufficient to change the top prediction.  
Figure 19: Proportion of images classified as a cup or a coffee mug in the top five predictions as a function of distance for different drinking vessel and camera combinations (scenario: Scenario 3)—subplots of interest highlighted with a gray face color.  
Figure 18: Proportion of images classified as a cup or a coffee mug in the top prediction as a function of distance for different drinking vessel and camera combinations (scenario: Scenario 3)—subplots of interest highlighted with a gray face color.  
Interestingly, with reference to Figures 18 and 19, the effect of transitioning from a prediction of coffee mug to cup with increasing distance for Vessel 3 in Scenario 1 is replicated in Scenario 3, but it was not apparent in Scenario 2. Again, this highlights the complex relationship between image-classification output, the camera used to acquire the image, and the exact scenario.