LAUSR

In the past, many researchers focus on scene classification in computer vision, because it is an important problem. Tourism scene classification, however, has not been paid attention to in the field of computer vision. In this paper, we introduce a new scenic-spots-centric database called tourism scene, which consists of 25 tourism scenic areas with 750 tourism scene categories, about 440 thousand labeled images. For tourism scene classification, we propose a multi-stage transfer learning model with category hierarchical structure and use convolutional neural networks (e.g., AlexNet) as basic building block. To demonstrate the effectiveness of our proposed model, we also propose a baseline model and one-stage transfer learning model. From the results, we observe that our proposed framework achieves new bounds for performance.