# Place Classification Algorithm Based on Semantic Segmented Objects
## 3 Experimental Results and Discussion
It shows that the highest test accuracy when the bias is 1.4. This value is used in the inference process when multiplying weights of background objects. In addition, we measured test accuracy on COCO-Stuff test dataset. We adopted first 100 images of the test images and labeled the images according to the criteria in Figure 4. We used same parameters as the previous experiment while training CNNs and building weight matrix. The result is shown in Table 3. The result shows that the proposed method outperforms the conventional CNNs and also indicates that it achieves better performance when test images are taken in the same domain with train images. \begin{table}
\begin{tabular}{c c} \hline \hline
**Models** & **Accuracy (\%)** \\ \hline VGG-19 [15] & 79.6 \\ VGG-19 (BN) [15] & 88.0 \\ GoogLeNet [16] & 84.6 \\ Inception-v3 [17] & 86.2 \\ ResNet-101 [18] & 84.0 \\ ResNeXt-101 [19] & 88.0 \\ Wide-ResNet [20] & 85.2 \\ DenseNet-121 [21] & 84.0 \\ DenseNet-161 [21] & 83.6 \\ DenseNet-201 [21] & 85.2 \\ MnasNet (0.5) [22] & 76.6 \\ MnasNet (1.0) [22] & 79.8 \\ \hline Proposed Method & 90.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison with the existing CNNs on indoor/nature/city classes. Figure 7: Experiment on different bias values for multiplying weights of background objects when inferencing. Lastly, we experimented on indoor classes and Table 4 shows the results. We used the subset of MITPlaces dataset [26]. It contains categories of 'library', 'bedroom' and 'kitchen' and 900 train images and 100 test images per class. As previous experiment, train images are used for building weight matrix and test images are used for measuring test accuracy in the proposed method. Classifying indoor categories must be treated differently from classifying indoor and outdoor. Since all indoor scenes have ceilings and walls, the bias value in Algorithm 2 must be given not by background objects, but by foreground objects. In this experiment, we defined foreground objects as furniture categories in Figure 2 and determined the value to be 3 empirically. Although the results shows that 7 of CNNs outperforms our method by less than 4.6%, it shows that our method can be extendable to indoor categorization problem. CNNs which showed the best performance tested with our custom dataset are VGG-19 (BN) and ResNeXt-101. They both showed test accuracy of 88% and the proposed method showed 90.8%. Table 5 represents the performance of three models on each of three scene classes. VGG-19 (BN) predicted all images perfectly in indoor class and the proposed method is following. In nature class, the proposed method showed the best accuracy. When it comes to city class, ResNext-101 showed the best results. From this result, we can see that the proposed method is reliable for scene classification. Source code is available at [[https://github.com/woonhahaha/place-classification](https://github.com/woonhahaha/place-classification)]([https://github.com/woonhahaha/place-classification](https://github.com/woonhahaha/place-classification)). \begin{table}
\begin{tabular}{c c} \hline \hline
**Models** & **Accuracy (\%)** \\ \hline VGG-19 [15] & 92.0 \\ VGG-19 (BN) [15] & 94.0 \\ GoogLeNet [16] & 88.0 \\ Inception-v3 [17] & 93.7 \\ ResNet-101 [18] & 91.0 \\ ResNeXt-101 [19] & 95.3 \\ Wide-ResNet [20] & 88.0 \\ DenseNet-121 [21] & 93.0 \\ DenseNet-161 [21] & 94.3 \\ DenseNet-201 [21] & 90.3 \\ MnasNet (0.5) [22] & 89.7 \\ MnasNet (1.0) [22] & 90.0 \\ \hline Proposed Method & 90.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison with the existing CNNs on library/bedroom/kitchen classes.