## 5 Publicly Available Datasets and Benchmarks
### Datasets for Scene Recognition  
This section presents datasets for scene recognition from [11], and their associated benchmarks, as illustrated in Table 2. These datasets are a combination of widely used datasets and some of them are new. A comparison of the recognition accuracies among some of the representative algorithms is studied, followed by a comprehensive analysis.  
Scene-15 This dataset contains 4485 gray images from 15 different categories that include both indoor and natural scenes [60]. The image sizes are relatively small, with 200 to 400 images per category. It is crucial to highlight that the recognition accuracy of certain CNN algorithms may be diminished for images lacking color information. In the absence of distinct training and test sets, random images, typically around 100 per category, are chosen for training, while the remaining images are employed for testing. For better evaluation results, it is suggested to perform random splits several times.  
SIN-397 SU is an acronym for scene understanding. This dataset comprises 397 distinct scene categories and 108,754 color images, with at least 100 images per category. These categories contain various indoor and outdoor scenes with larger objects and alignment variance and hence impose huge complexity for scene recognition tasks. One hundred images per category are chosen as the standard protocol [79], fifty of which are used for training with the remaining fifty images used for testing.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Scene** & \multirow{2}{*}{**Method**} & **Feature Retrieval** & **Scene-15** & **SUN-397** & **Indoor-67** & **Sports-8** \\ \hline \multirow{4}{*}{Global Attribute Descriptors} & GIST [13] & GIST & 73.28 & - & - & 82.60 \\  & LDBP [58] & LDBP & 84.10 & - & - & 88.10 \\  & mCENTRIST [57] & mCENTRIST & - & - & 44.60 & 86.50 \\  & CENTRIST [59] & CENTRIST & 83.88 & - & - & 86.22 \\ \hline \multirow{4}{*}{Patch Feature Encoding} & SPM [60] & SIFT & 81.40 & - & 34.40 & 81.80 \\  & DUCA [61] & AlexNet & 94.50 & - & 71.80 & 98.70 \\  & HIK [62] & CENTRIST & 84.12 & - & - & 84.21 \\  & MOP-CNN [63] & AlexNet & - & 51.98 & 68.88 & - \\  & LScSPM [64] & SIFT & 89.75 & - & - & 85.31 \\  & NNSD [65] & ResNet-152 & 94.70 & 64.78 & 85.40 & **99.10** \\  & LR-Sc+ SPM [66] & SIFT & 90.03 & - & - & 86.69 \\ \hline \multirow{4}{*}{Spatial Layouts Pattern Learning} & RSP [67] & SIFT & 88.10 & - & - & 79.60 \\  & S2 ICA [68] & VGG-16 & 93.10 & - & 74.40 & 95.80 \\  & RS-Pooling [69] & AlexNet & 89.40 & - & 62.00 & - \\ \hline \multirow{4}{*}{Discriminative Region Detection} & Object Bank [70] & Object Filters & 80.90 & - & 37.60 & 76.30 \\  & DSFL [71] & AlexNet & 92.81 & - & 76.23 & 96.78 \\  & VS-CNN [72] & AlexNet & **97.65** & 43.14 & 80.37 & 97.50 \\  & ISPRs [73] & HOG & 91.06 & - & 68.50 & 92.08 \\ \hline \multirow{4}{*}{Object Correlation Analysis} & SDO [50] & VGG-16 & 95.88 & 73.41 & 86.76 & - \\  & MetaObject-CNN [74] & Hybrid CNN & - & 58.11 & 78.90 & - \\ \hline \multirow{4}{*}{Hybrid Deep Models} & DAG-CNN [75] & VGG-19 & 92.90 & 56.20 & 77.50 & - \\  & Dual CNN-DL [76] & Hybrid CNN & 96.03 & 70.13 & 86.43 & - \\ \cline{1-1}  & FSNet [77] & SE-ResNeKt- & - & **77.28** & **90.37** & - \\ \cline{1-1}  & Hybrid CNNs [78] & VGG-19 & - & 64.53 & 82.24 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of accuracies of some of the scene recognition approaches on four different datasets from [11].