Architectural Style Classification Based on Feature Extraction Module

Abstract

Standard classification tasks have already achieved good results in computer vision. However, the task of Architectural style classification yet faces many challenges, since the rich inter-class relationships between different styles may disturb the classification accuracy. To better classify buildings, we propose a feature extraction module based on image preprocessed with Deformable Part-based Models (DPM). Specifically, we first use DPM to remove elements that are not related to classification, and capture representative elements of buildings, and then these elements are sent to our feature extraction module. In our feature extraction module, we adopt our improved ensemble projection method to maximize the inter-class distance and minimize the intra-class distance to find the common features in the same style and differences among different styles. Finally, the performances of several classifiers are tested and the best one of SVM classifier is selected to output the ultimate accuracy. Experimental results show that our approach achieves promising performance and is superior to previous methods.