## 7 Conclusions and Future Scope  
In this paper, we have proposed a segmentation-based attention model for indoor scene recognition that can assist visually impaired and elderly people with indoor navigation. Here, we have used the transfer learning concept of deep learning to develop our framework. We have implemented our work using a deep-pretrained DenseNet201 CNN architecture and a deep LSM (liquid state machine) model. We have evaluated our model on the NYU depth dataset of 20 classes of indoor scenes. We have tried to improve the robustness and performance of our model in varying indoor scenes by combining the advantages of a few techniques like fuzzy colour stacking, segmentation, and World Cup optimization. Preprocessing by the fuzzy colour stacking technique has helped improve the foreground quality of the image dataset. By adding the segmentation module, our model was able to handle the spatial details and semantic cues, providing a better region of interest in the indoor scenes. The pretrained DenseNet201 extracted the features from these segmented images. These features were filtered, and the most predominant features were selected using the World Cup optimisation algorithm. We have used a deep LSM (liquid state machine) model as our classifier, which efficiently classified the 20 classes of our indoor dataset. Our proposed model could achieve an accuracy of 96% on the NYU dataset. Thus, we could improve the robustness of our proposed model to recognise the different indoor scenes. In our future work, we will emphasise working with different datasets, including outdoor scenes, and combining various modalities in order to expand the relevance and applicability of our proposed work.  
Conceptualization, R.S.; Methodology, I.C.; Validation, D.J.H.; Formal analysis, J.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Data Availability StatementNot applicable. Conflicts of InterestThe authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**S. No.** & **Author** & **Methodology Used** & **Accuracy** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{Proposed Model} & Segmentation + DenseNet201 + World & \multirow{2}{*}{96\%} \\  & & Cup Optimization + LSM Classifier & \\ \hline \multirow{2}{*}{2} & [PERSON] et al. [52] & Semantic Segmentation + VGG16, & \multirow{2}{*}{75.8\%} \\  & [52] & ResNet18-50-101, DenseNet and & \\  & & MobileNetV2+ Feature fusion & \\ \hline \multirow{2}{*}{3} & [PERSON] et al. [53] & YOLO + TF + IDF & 83.63\% \\ \hline \multirow{2}{*}{4} & [PERSON] et al. [54] & 2D–3D geometric feature fusion + & \multirow{2}{*}{75\%} \\  & [54] & Graph convolutional neural network & \\ \hline \multirow{2}{*}{5} & [PERSON] et al. [55] & EfficientNet CNN model + Scaling & 95.6\% \\ \hline \multirow{2}{*}{6} & [PERSON] et al. [56] & MAPNet + Attentive pooling & 67.7\% \\ \hline \multirow{2}{*}{7} & [PERSON] et al. [57] & GoogleNet + Inception V3 + Feature fusion & 96\% \\ \hline \multirow{2}{*}{8} & [PERSON] et al. [58] & GoogleNet + Multi feature fusion & 92.92\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy comparison of existing works with our proposed model on indoor scene recognition.