## 2 Related Works
The authors of [38] used a detector-free transformer-based CNN model that extracts both global and local features simultaneously. They used the overlapping area prediction module to ensure a clean and effective aggregation of information. Inaccurate match labels were eliminated using the match label weight strategy. The authors of [39] have developed a novel semantic segmentation approach using a matric learning perspective that utilises only a few annotated examples, eliminating the requirement of numerous, densely annotated images. Ref. [40] effectively aggregated the local and global features using an attention-based fusion module. They also used a lightweight feature affine module and mapped the local feature to the normal distribution. They achieved superior performance on the SUN RGB-D and ScanNet V2 datasets. Some of the recent works on scene recognition are compared in Table 1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**S. No.** & **Author** & **Methodology Used and Results** & **Merits** & **Demerits** \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{[PERSON] et al. [41]} & Enhanced VHR attention module & \multirow{3}{*}{Rich discriminative salient features} & \multirow{3}{*}{Performance could be improved by using various pretrained models to classify new data.} \\  & & & & \\  & & (EAID) + atrous spatial pyramid pooling: global average pooling. & & \\  & & Accuracy of 95.39\% on AID and & & are achieved. & \\  & & & 93.04\% on NoViU dataset. & & \\ \hline \multirow{3}{*}{2} & \multirow{3}{*}{[PERSON] et al. [42]} & Segmentation + feature extraction & \multirow{3}{*}{Multi-object recognition against any varying environment.} & \multirow{3}{*}{Approach performs well for outdoor scenes when compared to indoor scenes.} \\  & & & (Using SegNet, VGG, DCT, DWT) + feature selection using genetic algorithm + neuro fuzzy classifier. Accuracy of & & \\  & & 96.13\% on Cityscapes, 63.17\% & & \\  & & SUN RGB D, and 72.8\% on NYU datasets. & & \\ \hline \multirow{3}{*}{3} & \multirow{3}{*}{[PERSON] et al. [43]} & CNN + spatial pyramid pooling. Accuracies of 71.75, 96.6%, & \multirow{3}{*}{Ensemble learning improves the overall performance.} & \multirow{3}{*}{Data augmentation is employed.} \\  & & and 98.17\% on Event-8, Scene-15, and & & \\  & & MIT-67 datasets & & \\ \hline \multirow{3}{*}{4} & [PERSON] et al. [44] & TrecgeNet and feature selection. Accuracy of 71.8\% on & \multirow{3}{*}{Model performs well with aligned colour and depth information.} & \multirow{3}{*}{Only single modality scene recognition is possible.} \\  & & NVI depth datasets. & & \\ \hline \multirow{3}{*}{5} & \multirow{3}{*}{[PERSON] et al. [45]} & Fuzzy c-mean, mean shift algorithm + logistic regression classifier. Accuracies of 88.75\%, 85.75\%, and 80.02\% on MSRC, & & \\  & & COREL 10K, and CVPR 67 datasets. & & \\ \hline \multirow{3}{*}{6} & \multirow{3}{*}{[PERSON] et al. [46]} & Pretrained ResNet CNN with feature-matching algorithm. & \multirow{3}{*}{Eliminates the problem of over fitting.} & \multirow{3}{*}{Performance reduces for images having varying illumination, scale, etc.} \\  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & Dense SIFT + Bow model + spatial pyramid pooling + binary classifier. & \multirow{3}{*}{Could classify scenes of different illumination and scaling.} & \multirow{3}{*}{Slow in execution.} \\ \cline{1-1}  & & ImageCLEF 2012 & & \\ \cline{1-1}  & & robot vision dataset. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Some recent existing works on scene recognition. Many of the state-of-the-art methods focus on video-based scene recognition because of the availability of spatial-temporal information from different video frames. Therefore, we have concentrated our work on still image scene recognition. Indoor navigation is a challenging task due to the complexity of the location and position of various objects of the same class. Many research studies on indoor scenes were carried out using image annotation and data augmentation to improve the model's performance.