Landmark image classification using 3D point clouds

Most of the existing approaches for landmark image classification utilize either holistic features or interest of points in the whole image to train the classification model, which may lead to unsatisfactory result due to involvement of much information non-located on the landmark in the training process. In this paper, we propose a novel approach to improve landmark image classification result via a process of 2D to 3D reconstruction and 3D to 2D projection of iconic landmark images. Particularly, we first select iconic images from labeled landmark image collections to reconstruct a 3D landmark represented in point clouds. Then, 3D point clouds are projected back onto the same iconic images to obtain the landmark-region of each iconic image and subsequently extract SIFT features from the landmark-region to construct a k-dimensional tree (kd-tree) for each landmark. This process is able to filter out noise points corresponding to clutter background and non-landmark objects in the iconic images. Finally, the unlabeled images can be classified into predefined landmark categories based on the amount of matched feature points between the image features and the kd-trees. The experimental result and comparison with the state-of-the-art demonstrate the effectiveness of our approach.