# Inferring Emotion Tags from Object Images Using Convolutional Neural Network
## 3 Results and Discussion
### Method of Analysis and Classifiers
#### 3.1.2 Performance Analysis for Inferring Emotion Tags at Bottom-Level  
This section provides the performance analyses of male, female, and combined emotion tag responses for bottom-level inferring emotions, as shown in Table 2. It is observed that the K-NN classifier is giving better results as compared to SVM and RF classifiers for three emotion tag responses. Likewise, the SVM classifier also gives better results for individual emotion tag male, female, and combined responses. On the other hand, the worst accuracy for three different scenarios is achieved with RF classifier, which is only 65% for inferring emotion tags based on male responses. It is investigated that gender-based emotion tag response performs better than the combined based emotion tag responses for K-NN, SVM, and RF classifier. This indicates that splitting the combined emotion tag responses into gender-wise emotion responses leads to better performance for inferring emotions. The best accuracy of 79% and 78% is achieved from male and female emotion tag responses, respectively. The SVM and RF classifiers depict the same performance for male, female and combine emotion tags based responses. For emotion tags at bottom-level, Figure 7 compares the best case F1-measure values of each of the emotion class achieved using K-NN. The F1-measure achieved for bottom-level emotion inferring with respect to male subjects is 0.789 using K-NN classifier, which is 0.042% and 0.147% higher than the SVM and RF classifiers, respectively. As shown in Figure 6, the F1-measure achieved for the boredom, contentment, disgust and sad emotion class for male-based emotion responses outperforms the female and combined emotion tag responses. Similarly, for the amusement, awe, and fear emotion class based on female responses, the value of F1-measure is higher than the male and combined emotion tag responses. On the other hand, the value of F1-measure achieved for the anger, and sad emotion class for the female-based emotion tag responses are low as compared to male and combined based emotion tag responses. Generally, females are fewer respondents to object categories includes in Caltech-256 object category dataset for the anger and sad emotion class as compared to males. It can be observed from Figure 8, that when using K-NN classifier in the case of male-based emotion tag inference at the bottom-level, the amusement emotion tag is truly classified 1835 times out of total 2567 instances. Likewise, in the case of female emotion tag responses for the amusement emotion category, 4782 instances are truly classified out of 6255 instances. On the other hand, in the case of combined emotion tag responses for the amusement category, only 2638 instances are truly classified out of 3562 in total. Also, for the sad emotion class, 468 instances are truly classified out  
Figure 7: Best-case average F1-score for inferring emotion tags at bottom-level with nine different emotions using K-NN. of 571 instances of male responses. For female and combined responses of the sad emotion class, 97 instances out of 491 and 162 out of 272 instances are truly classified respectively. The effective results for inferring emotion tags using the K-NN classifier indicate that it is easier to predict amusement, awe, boredom, excitement, disgust, fear, and contentment sentiments except for anger and sad tag based on object categories. In particular, the sad and anger sentiments vary from object to object both for males and females. Since the objects in the bottom-level of the Caltech-256 dataset has fewer categories, which perceive anger and sad emotions to both female and male while labeling the object categories with emotion tags. Specifically, for both male and female, emotion perception varies for the same object categories included in the Caltech-256 dataset. Thus, the results for combined emotion inferring are not as effective as compared to the results obtained from male and female based emotion tag results particularly.