# Inferring Emotion Tags from Object Images Using Convolutional Neural Network
## 3 Results and Discussion
### Method of Analysis and Classifiers
#### 3.1.1 Performance Analysis for Inferring Emotion Tags at Top-Level  
For analysis of the top-level emotion inferring, the chosen classifiers are trained to infer nine emotion categories. As discussed earlier, the collected responses from subjective labeling divided into three types of analyses for the top-level object categories. The illustration of the above three categories are as follows:  
* Combine analysis of inferring emotion tags: It is the analysis based on combined responses from both gender for Caltech-256 top-level object categories
* Analysis of inferring emotion tags for males: These are emotion responses that are collected from only males
* Analysis of inferring emotion tags for females: In this category, emotion responses are collected from only females. After grouping these emotion responses into three categories, we measure the proposed scheme performance for each category. Table 2 provides the average performance analysis for inferring emotion tags using K-NN, RF, and SVM classifiers. These results conducted with the help of three different analyses, i.e., male, female, and combine emotion responses. It is observed from Table 2 that for all three types of analysis at top-level, the K-NN classifier outperforms RF and SVM classifiers. However, SVM performs well in the cases when the margin between different class boundaries, i.e., hyperplanes, is sufficiently large. However, it is not possible in the case of our proposed scheme with small inter-class variations. In addition, the SVM classifier does not perform well if the dataset contains noise, such as labeling noise. In that case, target classes seem to be overlapping. Moreover, RF is an ensemble classifier that builds a collection of random decision trees (with random data samples and subsets of features), each of which individually predicts the class label. The final decision is made based on the majority voting principle. RF is inherently less interpretable than an individual decision tree. Hence, in the case of noisy-labeled data and an imbalanced dataset, RF may produce some random results. For our proposed scheme, the best accuracy is achieved by the K-NN classifier, and the worst performance is achieved using RF classifier for top-level inferring emotion tag. It can also be observed from the Table 2, inferring emotion tags based on male responses, we achieved the highest accuracy of 85.01%, which is 0.15% and 2.78% higher than the female and combined emotion tag responses. Similarly, female emotion tag responses for inferring emotion tags are better than combine emotion tag responses. Likewise, the RF and SVM classifiers showed similar performance for individual male, female, and combine emotion tag responses. Hence, gender information is effective for inferring emotion tags. Figure 5 shows the F1-measure values obtained for inferring emotion tag using KNN classifier. It can be observed from Figure 5 that the F1-measure value for the emotion classes such as boredom and awe, the males and females emotion responses outperform than the combined emotion tag results. However, in the case of other emotion classes, gender and combine emotion tag responses showed a quite similar value of F1-measure. It can be noted from Table 2 that for the top-level emotion tag responses of males, the highest value of F1-measure is achieved using the K-NN classifier, which is 0.01% and 0.92% higher than the F1-measure of SVM and RF classifiers respectively.