Architectural Style Classification Based on DNN Model

Deep neural networks (DNN) have been widely used for image classification. One major hurdle of deep learning approaches is that large sets of labeled data are necessary, which can be prohibitively costly to obtain, particularly in architectural style classification. Data augmentation can alleviate this labeling effort. In this paper, we use data augmentation to increase the number of architectural style datasets. To extract building elements, the inputs are preprocessed by Deformable Part Model (DPM) first, and then the preprocessed images are sent to the data augmentation to increase the number of images. Next, we design a deep neural network based on GoogLeNet. The proposed network aims to learn robust feature embeddings to improve architectural style classification performance. Finally, architectural style can be classified by the robust feature embeddings. Experimental results show that our approach achieves promising performance and is superior to previous methods.