Building Recognition Using Local Oriented Features

Building recognition is an important task for a wide range of computer vision applications, e.g., surveillance and intelligent navigation aid. However, it is also challenging since each building can be viewed from different angles or under different lighting conditions, for example, resulting in a large variability among building images. A number of building recognition systems have been proposed in recent years. However, most of them are based on a complex feature extraction process. In this paper, we present a new building recognition model based on local oriented features with an arbitrary orientation. Although the newly proposed model is very simple, it offers a modular, computationally efficient, and effective alternative to other building recognition techniques. According to a comparison of experimental results with the state-of-the-art building recognition systems, it is shown that the newly proposed SFBR model can obtain very satisfactory recognition accuracy despite its simplicity.