Multi-View Vocabulary Trees for Mobile 3D Visual Search

Abstract

Mobile Visual Search (MVS) is a research field which focuses on the recognition of real-world objects by using the mobile device such as smart phone or robots. Current mobile visual search solutions achieve search results based on the appearance and it is suitable for planar structured objects such as CD cover images, magazines and artworks. The novel solution Mobile 3D Visual Search captures not only the visual appearance of the query object, but uses also the underlying 3D geometry. Vocabulary Tree (VT) methods have been widely used to efficiently find the match for a query in the database with a large volume of data. In this thesis, we study the vocabulary tree in the scenario of multi-view imagery for mobile visual search. The experimental results show that our multi-view vocabulary trees improve the matching and ranking performance of mobile visual search.