Texture Gradient and Deep Features Fusion-Based Image Scene Geometry Recognition System Using Extreme Learning Machine

Image scene geometry recognition is an important task for reconstruct the 3D information of a single image which is beneficial for computer vision applications, such as 3D TV, video categorization. In this paper, a novel architecture for the image scene geometry recognition based on the feature-level fusion of convolutional neural networks (CNN) features and low-level texture gradient features is presented. The main advantages of using low-level features are; simple to extract and contain rich information of image scene geometry. Next, it is evaluated on a novel scene dataset that is constructed by following the twelve different image scene geometries (1000 samples for each category) and experimental results exhibit that proposed system achieves higher accuracy than applying the CNN alone. Additionally, by utilizing the extreme learning machine (ELM) as a classifier, the proposed system achieves 86.29% recognition accuracy that is superior the existing baseline methods.