# Place Classification Algorithm Based on Semantic Segmented Objects
## 3 Experimental Results and Discussion  
In this section, we will show the results of our classification model and well-renowned classification methods using CNNs. We implemented the proposed scheme by using PyTorch deep learning framework, and used single GPU for training. We trained DeepLab v2 model with COCO-stuff  
Figure 3: Construction of weight matrix \(W\) with a sample image. dataset which contains total 164k images. As we mentioned it in Section 2.2, the reason why we use COCO-stuff dataset is because its object classes are divided into indoor and outdoor categories. In order to compare our method with CNN based classification models, we first built a custom test dataset that consists of 500 images as shown in Table 1. We extracted them from Korean movies, and the images were manually labeled into three scene classes (i.e., 0: indoor, 1: nature, 2: city) under certain criteria. We set some criteria according to the logic that humans more focus on the background objects rather than foreground objects. The criteria are described in Figure 4.  
The sample test images are shown in Figure 5. We used COCO-stuff validation 2017 dataset for training the CNN models, which were also used for building the weight matrix. The test images were used for measuring accuracy of the classification. We experimented various CNNs, such as VGG [15], Inception [16; 17], ResNet [18], ResNeXt [19], Wide-ResNet [20], DenseNet [21], and MnasNet [22] as shown in Table 2.  
To be more specific, we trained each model by using transfer learning scheme [23], especially Feature Extraction [24; 25]. The basic concept of feature extraction is represented in Figure 6. Typical CNNs have convolutional layers for extracting good features and fully connected layers to classify the feature. Feature extraction technique which trains only fully connected layers is used when there are insufficient data for training. PyTorch Deep Learning Framework was used again to implement all structures for the experiment. The results of accuracy were computed after 200 iterations of training. We trained each model using cross entropy loss, and Adam optimizer with a batch size 64, learning rate 0.001. Learning rate was multiplied by 0.1 every 7 iteration. In Table 2, we can observe the proposed scheme outperforms the existing well-known CNNs which were trained using transfer learning. In terms of the accuracy, the proposed scheme achieved 90.8% of the accuracy. Figure 4: The criteria of labeling custom dataset. We first label the scene as either indoor or outdoor according to the existence of ceiling and surrounding wall. Then city images are labeled by buildings and pavements which are usually existing in urban area. Lastly, the scene is labeled as nature when there is nature stuff such as bush, mountain, sea, or river. Figure 5: Samples of custom test dataset; each row represents each scene class. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indoor** & **Nature** & **City** & **Total** \\ \hline
305 & 106 & 89 & 500 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The number of images for each class in our custom dataset. Figure 6: Basic Concept of Feature Extraction. The white box represents convolution and pooling layers in convolutional neural networks (CNNs), which are used for extracting features of an image. This part is not trained during transfer learning process. The green box represents fully connected layer in CNNs, which operates as classifier. This part is trained during whole training process. When compared with VGG-19 (BN) [15] and ResNeXt-101 [19], the proposed method could improve 2.8% of the accuracy. Also, our scheme improved the performance over 13% comparing to MnasNet (0.5) [16]. VGG-19 was tested with batch normalization (BN) and without BN. The float values with MnasNet is the depth multiplier in Reference [22]. From this result, we can see that the proposed scheme is very reliable and better to classify the scene. Figure 7 represents the graph of the experiment on determining optimal bias value in terms of test accuracy.