Scene Recognition Using Convolutional Neural Networks Assignment Sample

Implementing a CNN with ResNet50 for Scene Recognition

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Introduction

This paper offers an analysis of scene recognition utilizing Convolutional Neural Networks (CNNs) in which some place image dataset has been applied corresponding to the task. Besides, the primary goal of this research is to explore the utilization of CNNs for scene recognition tasks, with the expectation of achieving improved accuracy and improved generalization performance. Further, the study outlines the various models like ResNet or others used for scene recognition using CNNs, as well as it reviews multiple implementations of CNNs for scene recognition. The study also discusses methodology corresponding to the task for which researchers can better understand the task results that are obtained from the various experiments conducted, and provides insights into the potential future directions for further research in the field of scene recognition using CNNs.

Literature

In the past few decades, Convolutional Neural Networks (CNNs) have made great progress in the area of scene recognition or fields for which people can enhance their lifestyle in a better way. CNNs are a type of deep learning model, which are able to learn hierarchical representations of the input data and are very useful for image classification tasks (Pires de Lima and Marfurt, 2019). The fundamental building blocks of a CNN are an input layer, a number of convolutional layers, layers for pooling, layers that are completely connected, as well as an outcome layer. Apart from that, CNNs have been extensively used in the field of scene recognition due to their ability to learn high-level features from the input image. As stated by Pires de Lima and Marfurt, (2019), they applied a CNN to recognize the semantic information of scenes from a remote sensing image. The authors used a VGG-16 model as the base network and fine-tuned the model on the remote sensing dataset. The outcomes demonstrated that the suggested strategy outperformed other cutting-edge techniques, achieving an accuracy of 0.922 on the test set.

In addition, CNNs have also been applied to recognize the contextual information of scenes. For instance, in the work of Xie et al. (2018), they proposed a CNN-based approach to recognize the contextual information of a scene from a single image. The authors used a ResNet-50 model as the base network and fine-tuned the model on the scene recognition dataset. The outcomes demonstrated that the suggested strategy surpassed other cutting-edge techniques, achieving an accuracy of 0.937 on the test set.

In conclusion, CNNs have shown great potential in the field of scene recognition and have achieved state-of-the-art results on several benchmark datasets. The success of CNNs in the field of scene recognition is due to their ability to learn high-level features from the input image, which enables them to recognize the semantic and contextual information of scenes (Xie et al. 2019). Moreover, CNNs can be further improved by incorporating additional techniques, such as data augmentation and transfer learning, which can help to boost the performance of the model.

Methodology

This study also elaborates the methodology utilized in order to create a scene recognition system using convolutional neural networks (CNN) with Google Collab and ResNet50 model. Besides, the overall purpose of this project is to generate a deep learning model that helps to identify accurately the scene in an image.

In this case, the first step is to set up the Google Collab environment. This helps to utilize the ResNet50 model and to run the CNN code (Koutini et al. 2019). Therefore, once the environment is set up, the ResNet50 model will be imported into the Collab platform. This dataset consists of a large number of images of different scenes.

Next, the CNN model will be created and trained. The CNN model consists of a series of layers that are used to extract the features in the images. The model utilized training on the ResNet50 model. The model will be trained using a supervised learning algorithm, where the labels of the images are utilized to guide the model in learning the features of the images.

Once the model has completely trained, it will be tested on a test set. Besides, this test set consists of images that the model has not seen before. The CNN model performance helps to evaluate based on the accuracy of its predictions (Rashid et al. 2019). If the accuracy is satisfactory, the model can then be used for scene recognition.

Finally, the model can be used to recognize scenes in images. The model will take an image as input and will output a prediction of the scene in the image. This prediction can then be used to classify the image. In addition to that, this paper has discussed the methodology utilized in order to create a scene recognition system using CNNs with Google Collab and ResNet50 model (Liu et al. 2019). The model has been trained by utilizing a supervised learning algorithm as well as tested on a test set.

Experiments

Figure 1: Import necessary libraries and load the place images from the drive

In this case, train images are loaded from the drive and necessary python libraries are imported for which the scene recognition system runs accurately. The data class variable has been utilized here to create an image dataset from a directory of images. The 'place_image' argument is the path to the directory containing the images. The 'data_classes' variable will contain the dataset that is created, which can be used for training a model.

Figure 2: Create function to view the image from the train set

This code has been shown a function, train_image_visualization, that takes in a data class as well as generates a figure with five rows or five columns of images from the place dataset. The function utilizes a for loop in order to set the image numbers that are displayed in the output terminal, as well as it utilizes another for loop to plot images from the dataset.

Figure 3: Build the CNN with ResNet model

The above codes are implemented to build the CNN with ResNet model for scene recognition. Further the above codes help to creates a Convolutional Neural Network model utilizing the ResNet50 architecture, with a Conv2D, MaxPooling2D, Flatten, and two Dense layers, and compiles it with Adam optimizer and categorical cross entropy loss. The model is trained with an ImageDataGenerator in a validation split of 0.2, with a batch size of 32, and the images are resized to a target size of (108,108), and in a 'rgb' color mode. The last layer of the model has a SoftMax activation with 40 classes (McDonnell and Gao, 2020).

Figure 4: View CNN model summary

The following image shows the CNN model summary after building the model successfully.

Figure 5: View Model Accuracy

The model accuracy is shown as 93% after fitting the model corresponding to the scene recognition.

Figure 6: Generate graph for Accuracy value for the CNN model

This code utilizes the Python library plotly for creating a graph that visualizes the accuracy of a Convolutional Neural Network (CNN) model with ResNet. The graph plots the accuracy values from the CNNmodel_history_check.history dictionary for both the training as well as validation data. Apart from that, this code is useful to users as it enables them to visually compare the performance of the CNN model on the training and validation data and identify any potential issues with the model.

This code utilizes the ploty library for plotting a graph of the loss values of the CNN with the ResNet Model over the Total Epoch. It delivers a visualization of the loss values over the epochs of the model, allowing the user to gain insights into how the model is performing. The legend indicates the values being plotted, and the title and labels provide clarity on the graph.

These codes have been applied in order to generate a confusion matrix for a convolutional neural network model. The code uses the predict function of the CNN model to generate predictions for test data generated by a testgenerator. The np.argmax function is then used to extract the highest values from the predictions and store them in a new array. The true classes of the test generator are then stored in a new array. The confusion_matrix function is used to generate a matrix based on the predicted and true classes (Dhillon and Verma, 2020). The sns.heatmap function is then used to generate a heatmap of the confusion matrix with annotations. The last two lines set labels for the x and y axes.

This code helps users to visualize the performance of their CNN model by showing the confusion matrix. The heatmap allows users to quickly identify where the model is making mistakes and helps them to decide how to improve the model.

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Conclusion & Future Work

It can be concluded that this research paper has examined the application of CNN with ResNet for scene recognition. Through literature review, different types of approaches for scene recognition as well as their associated challenges are described for better understanding about the topic. The methodology utilized in this research has been based on the implementation of CNN with ResNet for scene recognition. The analysis showed that the proposed model is able to surpass the current state-of-the-art results in scene recognition. In conclusion, CNN with ResNet model is an effective approach for scene recognition and can be used for real-world applications.

In the future, more advanced models or methods, such as transfer learning, may be explored in order to enhance the performance of CNN with ResNet for scene recognition. In addition to that, the exploration of the utilization of more sophisticated architectures, such as the “Inception-ResNet-v2 and MobileNet-v2”, can be conducted to further improve the accuracy of the model. Additionally, to improve the model's robustness, several data augmentation techniques including “rotation, scaling, as well as flipping” might be investigated. In order to further increase the model’s accuracy, several optimization strategies, such as “neural networks”, might be explored. Finally, the exploration of different assembling techniques, such as “bagging and boosting”, can be conducted to further improve the accuracy of the model.

Referencess

Dhillon, A. and Verma, G.K., 2020. Convolutional neural network: a review of models, methodologies and applications to object detection. Progress in Artificial Intelligence, 9(2), pp.85-112.

Koutini, K., Eghbal-Zadeh, H., Dorfer, M. and Widmer, G., 2019, September. The receptive field as a regularizer in deep convolutional neural networks for acoustic scene classification. In 2019 27th European signal processing conference (EUSIPCO) (pp. 1-5). IEEE.

Liu, Z., Shi, S., Duan, Q., Zhang, W. and Zhao, P., 2019. Salient object detection for RGB-D image by single stream recurrent convolution neural network. Neurocomputing, 363, pp.46-57.

McDonnell, M.D. and Gao, W., 2020, May. Acoustic scene classification using deep residual networks with late fusion of separated high and low frequency paths. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 141-145). IEEE.

Pires de Lima, R. and Marfurt, K., 2019. Convolutional neural network for remote-sensing scene classification: Transfer learning analysis. Remote Sensing, 12(1), p.86.

Rashid, M., Khan, M.A., Sharif, M., Raza, M., Sarfraz, M.M. and Afza, F., 2019. Object detection and classification: a joint selection and fusion strategy of deep convolutional neural network and SIFT point features. Multimedia Tools and Applications, 78, pp.15751-15777.

Xie, J., He, N., Fang, L. and Plaza, A., 2019. Scale-free convolutional neural network for remote sensing scene classification. IEEE Transactions on Geoscience and Remote Sensing, 57(9), pp.6916-6928.