Binary Classification of Visual Scenes Using Convolutional Neural Network
Aya M. Shaaban, Walid Al‐Atabany, Nancy M. Salem · 2019
Scene classification is a dominant track in computer vision tasks as it can help in many missions such as navigation, preprocessing, big data organization, albuming systems, and recognition applications for blinds. Recently, Convolutional Neural Network (CNN) shows noteworthy performance in enhancing the results of most image processing research points. In this paper, we use CNN for indoor-outdoor classification problem with the aid of a large database. Inception-v3 model (without its top layers) is used to extract scene features and extra three layers are attached to adopt the classification task. Our approach reaches an overall classification accuracy of 98.4% which shows the robustness of CNN over the old techniques.