Supervised training database by using SVD-based method for building recognition

This paper describes an approach to build a common model of building from different viewpoints. Then we apply to recognize building surfaces. For each image, buildingpsilas characters such as facets, areas, hue color histogram and a list of local features are calculated by our previous works. All correspondent facets are selected by supervision of user when the database is training. To calculate the characters of common model, we proposed a new method by using singular value decomposition (SVD). Given two or more similar vectors, SVD-based method computes an approximate vector which not only represents to the components but also automatically reduces the random noise. By using the common model, the number of facets and local features in the database are remarkably reduced. Therefore, the recognition rate is improved.