# Effect of Camera Choice on Image-Classification Inference
## 4 Results and Discussion
### Scenario 1
To gain more insight into this observation, we define a metric to quantify the ability of the pretrained Resnet-18 model to distinguish between the two similar classes of cup and coffee mug. The selectivity \(\sigma\) is defined formally in Equation (1) and is the mean absolute difference between the prediction probabilities for the cup and coffee mug classes. \[\sigma(s,v,c,d)=\frac{1}{N}\sum_{j=1}^{N}\left|P_{cup}(s,v,c,d,j)-P_{mug}(s,v, c,d,j)\right| \tag{1}\]  
Figure 12: Proportion of images classified as a cup or a coffee mug in the top five predictions as a function of distance for different drinking vessels and camera combinations (scenario: Scenario 1). where  
* \(P_{cup}\) is the prediction probability for the cup class provided by the pretrained Resnet-18 model for image \(j\) corresponding to the tuple (\(s\), \(v\), \(c\), \(d\));
* \(P_{mug}\) is the prediction probability for the coffee mug class provided by the pretrained Resnet-18 model for image \(j\) corresponding to the tuple (\(s\), \(v\), \(c\), \(d\));
* \(s\) is the scenario which defines the image background;
* \(v\) is the vessel that is the image target;
* \(c\) is the camera acquiring the image;
* \(d\) is the distance between camera and vessel;
* \(N\) is the number of images in the dataset corresponding to the tuple (\(s\), \(v\), \(c\), \(d\)). Figure 13 illustrates bar charts showing the selectivity of the pretrained Resnet-18 model as a function of distance for different combinations of drinking vessel and camera. To examine the differences in selectivity specifically between the Angetube962, Logitech C920e, and Microsoft Lifecam as three cameras with similar FOVs, we reformat the graphs of Figure 13 as grouped bar charts in Figure 14. It is clear from Figure 14 that the selectivity of the image classifier to distinguish between a cup and a coffee mug is usually higher (and usually significantly higher) for images acquired by the Angetube 962A than the Microsoft LifeCam. This demonstrates a significant dependence on the performance of the image classifier in Scenario 1 on the camera used to acquire images. This dependence of selectivity on the camera does not, for this scenario, translate into a corresponding dependence of the classification output in terms of the top one and top five predictions (illustrated in Figures 11 and 12, respectively) on the camera; this is primarily because, although the selectivity values are significantly different for the different cameras, they are usually quite high, which means the image classifier is easily able to distinguish between a cup and coffee mug for this scenario. Some other interesting observations can be made about Figure 14. The selectivity is very low for Vessel 1 at 20 cm for all three cameras. This may reflect the fact that Vessel 1 is relatively large and, therefore, occupies a large section of the image space at this distance. It can be seen from Figure 13 that the selectivity for Vessel 1 at 20 cm is significantly higher when acquired with the EMEET SmartCam C960, for which the larger FOV makes the apparent size of the vessel smaller. Separately, for Vessel 3, the selectivity is minimum for all three cameras at the intermediate distance of 30 cm; this corresponds to the distance at which the image classifier predictions are transitioning from \"coffee mug\" to \"cup\" for these three cameras in Figure 11.