## 3 Proposed Method  
Conventional space ambiance extraction methods based on BoVW represent space as a set of VWs created from local features [22]. Thus, conventional methods cannot account for connections among local features or color information. Therefore, we propose a new method that focuses on the frequency of similar local features, color information, and connections between object features. Specifically, we use histograms, color information of each pixel, SPM, object detection (OD), color vector, and color histograms. Figure 3 shows an overview of the proposed methods. The algorithms of the proposed method are shown below.  
**Step-1: Preprocessing**: Resize each \(L_{ij}\) of training images \(L=\{L_{ij};i=1,\ldots,I,j=1,\ldots,J\}\) to \(256\times 256\) to create the training images \(T=\{T_{ij};i=1,\ldots,I,j=1,\ldots,J\}\). Note that there are \(J\) pieces in each of the \(I\) styles.
**Step-2: Extract Local Features** Extract 64-dimensional SURF \(S_{ij}=\{S_{ij}^{k};k=1,\ldots,K\}\) for each \(T_{ij}\).  
Figure 1: Overview of BoVW.  
Figure 2: Overview of SPM.  
* **Create VWs** Cluster the local features \(\mathbf{F}\) of all training images to create VWs \(W_{ij}=\{\mathbf{W}_{ij}^{l}\cdot l=1,\dots,L\}\) of the training images \(\mathbf{T}_{ij}\).
* **Create histograms** Create histogram \(\mathbf{H}_{ij}=\sum_{l=1}^{L}W_{ij}^{l}\) for each training image \(\mathbf{T}_{ij}\) based on \(\mathbf{W}\) from the local features \(\mathbf{F}_{ij}\). Then, normalize the histogram \(\mathbf{H}_{ij}\) and calculate the image features \(\mathbf{H}_{ij}=\frac{H_{ij}}{\sum H_{ij}}\) for each training image.
* **Extraction of representative colors** Cluster the color information of each training image \(\mathbf{T}_{ij}\) by the number of clusters \(\mathbf{O}\) and extract representative color vector \(\mathbf{C}\mathbf{\tilde{V}}_{ij}\) = \(\mathbf{C}\mathbf{\tilde{V}}_{ij}^{o}\mathbf{o}=\mathbf{1},\dots,\mathbf{O}\). Then, the representative color vector \(\mathbf{C}\mathbf{\tilde{V}}_{ij}\) is normalized by the maximum value in the color space, and the representative color vector \(\mathbf{CV}_{ij}\) is calculated.
* **Create color histogram** Color histogram \(\mathbf{C}\mathbf{\tilde{H}}_{ij}=\sum o=1^{O}\mathbf{CV}_{ij}^{o}\) based on the representative color vector \(\mathbf{CV}_{ij}\) is created from color information in each training image \(\mathbf{T}_{ij}\). Then, normalize \(\mathbf{C}\mathbf{\tilde{H}}_{ij}\) to calculate \(\mathbf{CH}_{ij}=\frac{c\mathbf{H}_{ij}}{\sum CH_{ij}}\).
* **Create low-level regions**\(N_{ij}\) objects are detected from each training image \(\mathbf{T}_{ij}\) using YOLO to create region \(\mathbf{R}_{ij}=\{\mathbf{R}_{ij}^{n};n=1,\dots,N_{ij}\}\).
* **Create features for low-level regions** Create histogram \(\mathbf{HL}_{ij}^{n}\), color vector \(\mathbf{CV}\mathbf{L}_{ij}^{n}\), and color histogram \(\mathbf{CH}\mathbf{L}_{ij}^{n}\) for \(\mathbf{R}_{ij}^{n}\) as in Steps 2-6.
* **Create image features for low-level regions** Histogram \(\mathbf{HL}_{ij}^{n}\), representative color vector \(\mathbf{CV}\mathbf{L}_{ij}^{n}\), and color histogram \(\mathbf{CH}\mathbf{L}_{ij}^{n}\) multiplied by the weight \(\mathbf{w}\) in each low-level region are concatenated to create image features \(\mathbf{IF}\mathbf{L}_{ij}^{n}=\mathbf{w1 HL}_{ij}^{n}\oplus\mathbf{w2}\cdot\mathbf{CV}\mathbf{L}_{ij}^ {n}\oplus\mathbf{w3}\cdot\mathbf{CH}\mathbf{L}_{ij}^{n}\).
* **Create image features for scene images** Create image features \(\mathbf{IF}_{ij}=\mathbf{w1H}_{ij}\oplus\mathbf{w2}\cdot\mathbf{CV}_{ij}\oplus\mathbf{w3}\cdot \mathbf{CH}_{ij}\oplus\mathbf{IF}\mathbf{L}_{ij}\) for each training image \(\mathbf{T}_{ij}\) based on the image features \(\mathbf{IF}\mathbf{L}_{ij}^{n}\) of the low-level regions.