## 2 Related Works  
Indoor scene recognition has gained great attention in recent years, and many research projects have been developed in this field. Most methods of scene recognition consist of a three-step approach, which consists of learning the features in different positions and scales of the objects in the scene, pooling or cumulating these learned features in order to obtain the feature description, and, based on the feature representation, learning a suitable classifier. Traditional indoor scene recognition techniques mainly focus on global features of the scene. In these approaches, low-level features like edges, colours, and textures [5] are used for scene classification. For the initial step, the traditional state-of-the-art methods used handcrafted methods like SIFT [6] (scale invariant feature transformation), SURF [7] (speeded up robust features), GIST [8], and HOG [9] (histogram of oriented gradients). Later, mid-level feature extraction using BoVW [10] (Bag of Visual Words) was employed to improve the extracted features. In the next phase, these extracted local descriptors with different scales and locations are aggregated using encoding pooling methods such as FV [11] (Fisher Vector) and VLAD [12] (Vector of Locally Aggregated Descriptors). In the last phase, based on the feature representation, some widely used classifiers such as SVM (Support Vector Machine), KNN (K-nearest neighbour), neural networks, etc. are employed for the scene recognition task. These traditional methods employ the use of handcrafted image features for classification. Deep learning [13] has become a better solution to improve the performance of many machines in understanding specific computer vision tasks. Unlike conventional machine-learning approaches, where the features for a specific vision task are manually extracted by domain experts, deep learning methods are capable of extracting the feature representations automatically from the raw images or data. Deep learning methods extract high-levelinformation required for recognition tasks. Deep learning architecture consists of deep neural networks (DNN), which are organised into multi-layered hierarchical trainable layers. As the raw data, given as input to the DNN, flow through each intermediate layer, multiple feature representations are obtained. Each layer learns specific features, with the first several layers learning the low-level features and the deeper layers learning the high-level features. In this manner, the raw data are eventually learned by the multiple layers and are represented by a multi-dimensional feature vector. Thus, deep learning is capable of discriminating different patterns and plays a vital role in scene recognition tasks. A convolutional neural network [14] (CNN) is one of the commonly used architecture models of deep learning. CNN is a multilayered neural network architecture that mimics the human brain. It consists of convolutional layers, a pooling layer, a non-linear rectified linear unit (ReLU) layer, and a fully connected layer. Convolutional layers are responsible for extracting characteristic features, depending on the number of hidden layers. The pooling layer does the downsampling and reduces the size of feature vectors. The ReLU layer replaces all the negative values with 0 and preserves network stability. The final fully connected layer collects all the high-level features and classifies them as labels. Deep learning gained attention in different areas of computer vision tasks with the success of Alexnet [15], trained on the ImageNet [16] dataset. There are many different popular deep CNN models available for scene recognition tasks, including SqueezeNet [17], VGG16 [18], GoogleNet [19], ResNet [20], DenseNet [21], etc. Some of the commonly used indoor and outdoor datasets are NYU [22], Scene [23], Places [24], SUN [25], MIT 67 [26], etc. The availability of powerful processors and large-scale datasets has improved the efficiency of many deep learning models.