Pruning near-duplicate images for mobile landmark identification: A graph theoretical approach

Abstract

Automatic landmark identification is one of the hot research topics in computer vision domain. Efficient and robust identification of landmark points is a challenging task, especially in a mobile context. This paper addresses the pruning of near-duplicate images for creating representative training image sets to minimize overall query processing complexity and time. We prune different perspectives of real world landmarks to find the smallest set of the most representative images. Inspired from graph theory, we represent each class in a separate graph using geometric verification of well-known RANSAC algorithm. Our iterative method uses maximum coverage information in each iteration to find the minimum representative set to reduce and prioritize the images of the initial dataset. Experiments on Paris dataset show that the proposed method provides robust and accurate results using smaller subsets.