Improving Street View Image Classification Using Pre-trained CNN Model Extracted Features

This paper presents a new approach for the challenging problem of image geo-localization using Convolutional Neural Networks (CNNs). This latter has become the state-of-the-art technique in computer vision and machine learning, particularly in location recognition of images taken in urban environments where the recognition accuracy is very impressive. We cast this task as a classification problem. First, we extract features from images by using pre-trained CNN model AlexNet as a feature extraction tool; where the output of the fully connected layer is considered as the feature representation. Then, the features extracted from the fully connected layer can be used for the classification process by feeding them into the Support Vector Machine (SVM) classifier. We evaluated the proposed approach on a data set of Google Street View images (GSV); the experimental results show that our approach can improve the classification by achieving a good accuracy rate which is 94.19%.

• street view images
• Google Street View
• AlexNet
• pre-trained CNN features
• feature extraction
• support vector machine
• place recognition
• urban image

• Advanced Image and Video Retrieval Techniques
• Advanced Neural Network Applications
• Image Retrieval and Classification Techniques
• street view images
• Google Street View
• AlexNet
• pre-trained CNN features
• feature extraction
• support vector machine
• place recognition
• urban image
• Computer Vision and Pattern Recognition
• Computer Science