## 2 Related Works
A recent development in deep learning, known as transfer learning, has enhanced the use of deep CNN models in many research applications. Deep models can show their performance when trained with large datasets using powerful GPUs (Graphical Processing Units). But, for some applications, the available dataset is limited. In such situations, we can use the concept of transfer learning to train our deep models. Here, the previously trained deep models transfer their learned parameters to the new learning task, even using a small dataset. The weights of these pretrained models already trained with the large dataset automatically update with the new task of a different dataset. Therefore, recent research has used the concept of transfer learning-based deep models for indoor scene recognition tasks. In recent years, much research has been conducted in the field of indoor scene recognition. Traditional methods of indoor scene recognition include handcrafted features for classification like texture, colour, etc. In [27], focus is given to these features to classify the images of landscapes and cities. The method worked well for outdoor images, but it faced difficulties in recognising indoor scenes. The authors of [26] solved this problem by combining the global and local features using GIST descriptors and a spatial pyramid of visual words on the MIT-67 dataset. In [28], classification was carried out on the LabelMe dataset by using objects as the feature attribute and SVM as the classifier. In [29], the authors used a probabilistic model using objects as feature representations. They used Adaboost classifiers operated on HOG, grey-level features, and Gabor. Recently, deep learning approaches have been used more than traditional methods. In [30], a deep CNN architecture with a linear SVM classifier was employed on the Scene 15 and MIT-67 datasets and attained accuracies of 90% and 68.24%, respectively. The authors of [31] utilised the methodology of mid-level convolutional features and an SVM classifier on the MIT-67, Scene 15, and NYU datasets and attained accuracies of 74.4%, 93.1%, and 81.2%, respectively. The authors in [32] have employed a novel methodology using scale-invariant and spatial layout convolution activations and an SVM classifier, and they obtained an accuracy of 81.2% on the NYU dataset. The authors of [33] have proposed a model based on the fusion of transcribed speech with visual and text features using videos of indoor scenes. They achieved accuracies of 70% and 74% on InstaIndoor dataset and YouTubeIndoor dataset, respectively. The researchersin [34] proposed a novel method using GAN and deep CNN for indoor object detection. They employed the Honey Adam African Cultures Optimisation (HAAVO) algorithm to estimate the optimum distance and attained an accuracy of 94% on my nursing home dataset. The authors of [35] proposed a semantic region relationship model using ResNet50 that employs only semantic segmentation results for indoor scene recognition. They evaluated their performance on the MIT-67, Places365-7, Places365-14, and SUN RGBD datasets with 81.64%, 93.143%, 86.714%, and 76.119% accuracy, respectively. In [36], the authors employed the MRNet using Resnet50 for scene recognition by considering triple information, such as local object information, local scene information, and global scene information. They employed class activation mapping (CAM) to obtain salient features and used LSTM. They achieved accuracies of 96.14%, 88.08%, and 73.98% on Scene 15, MIT 67, and SUN 397, respectively. The concept of local feature matching was employed by the researchers to overcome some of the challenges of indoor and outdoor scene recognition. The authors of [37] developed a deep-transformer-based network to achieve efficient local feature matching. They also employed a slimming transformer and feature transition module, and they achieved better results by generating robust and accurate matches for both indoor datasets (ScanNet) and outdoor datasets (MegaDepth).