Predicting good features using a hybrid feature for visual geolocation system

We address the problem of accurately geolocating an image on a large city scale. Image geolocation is the process of distinguishing a place in an image through geotagged reference images depicting the same place. This is a challenging task due to the appearance changes in large outdoor environments. In particular, the limitation on using large geotagged images effectively for training. To overcome this limitation, we propose to select and predict good hybrid features, and cast the prediction score as a classification task. To this end, we generate training features and learn the classifier offline. For the image representation phase, we propose a new method called hybrid feature to make image representation robust against geometric and photometric changes and have a high discriminative level as well. By doing this, we achieve competitive results compared with other baseline methods. Also, our results show a significant improvement while using hybrid features compared to using handcrafted models or deep learning methods individually.