Robust image retrieval using multiview scalable vocabulary trees

Abstract

Content-based image retrieval using a Scalable Vocabulary Tree (SVT) built from local scale-invariant features is an effective method of fast search through a database. An SVT built from fronto-parallel database images, however, is ineffective at classifying query images that suffer from perspective distortion. In this paper, we propose an efficient server-side extension of the single-view SVT to a set of multiview SVTs that may be simultaneously employed for image classification. Our solution results in significantly better retrieval performance when perspective distortion is present. We also develop an analysis of how perspective increases the distance between matching query-database feature descriptors.