# Deep Convolutional Neural Network with KNN Regression for Automatic Image Annotation
## 4 Experiments and Result Analysis
### Scenario 2: Comparing Our Method to the State of the Art
Now, if we look at the scenario of 374 concepts, we can see that our proposed method has surpassed all other methods except for that of [PERSON] et al. (2020) [85]. However, if we consider the method of [PERSON] et al. in terms of N+, we can see that our method outperforms it by eight concepts. This means that our method is capable of appropriately assigning eight more concepts than the method of [PERSON] et al. As previously said, it is not sufficient for a technique to achieve high accuracy alone; it should also acquire the meaning of the greatest number possible of concepts. To further analyze the outcomes of our method, we have calculated statistics of P, R, and F1 and presented them using a box plot. Figure 7 presents some statistics about how our proposed method learns the meaning of concepts. From the first glance, it appears that there is a compromise between precision and recall based on the used number of concepts. With 374 concepts, for instance, our system achieved a recall that is far higher than the precision. When it comes to 260 concepts, however, the precision remarkably improved whereas the recall slightly decreased. As the depicted standard deviation (\(\approx\)14 in both cases) indicates, our proposed technique aids in the balanced learning of various concepts. With a median of 45.7 in the scenario of 374 concepts, our findings indicate that more than half of the images were annotated with at least two to three accurate concepts, which is a significant number given a large number of concepts (374 concepts). Nonetheless, the number of correctly annotated images with two to three concepts increases substantially in the case of 260 images, resulting in a 75% rate. It should be noted that manually annotating images involves some subjectivity or mistakes, which results in the appearance of certain outliers, as seen in Figure 7.  
On one hand, the approach proposed in the work of [PERSON] et al. (2016) [16] relies totally on finding the semantic relatedness among presegmented regions based on a wide range of handcrafted features [86; 87]. By understanding the logic that connects different concepts, the system became able to learn concepts regardless of their narrow use. On the other hand, the idea in the work of [PERSON] et al. (2020) [72] revolves around  
Figure 7: Statistical description of how our proposed method learns the meanings of concepts accurately and in a balanced manner. Precision and recall are denoted by the letters P and R, respectively, and the following number denotes the number of concepts utilized in the experiment. employing CNN as a black box and letting it learn everything by itself. However, we have taken advantage of both the methods by applying a CNN to obtain a rich set of features representing the concepts and employing KNN regression to understand how these concepts are related. By doing so, we have exceeded the performance of both previous techniques. MSRC v2 dataset has also been used to assist the performance of AIA systems in various literature works, in particular those based on regions. We have conducted a comparison against some recent works on the same dataset using the same 22-concept scenario. Due to the limited number of annotations (22 concepts only), the metric N+ has been disregarded in this comparison since it always produces the perfect result (i.e., N+ = 22). Figure 8 presents F1 in terms of precision and recall using the MSRC v2 dataset. Figure 8 clearly shows that our proposed method outperforms the others by yielding precision = 78.01% and recall = 82.6% which produce the highest F1 score of 80.24%. However, assessing the method's performance based on a sample mean of precisions is, in many cases, deceptive. Therefore, it is a common practice in AIA performance assessment procedure to evaluate the performance on each concept individually. Figure 9 presents a precision heatmap yielded by our method compared to the others. As it appears from Figure 9, CNN-THOP and our method have outperformed the others by yielding perfect precisions with four concepts.