Large scale sketch based image retrieval using patch hashing

Abstract

This paper introduces a hashing based framework that facilitates sketch based image retrieval in large image databases. Instead of exporting a single visual descriptor for every image, an overlapping spatial grid is utilised to generate a pool of patches. We rank similarities between a hand drawn sketch and the natural images in a database through a voting process where near duplicate in terms of shape and structure patches arbitrate for the result. Patch similarity is efficiently estimated with a hashing algorithm. A reverse index structure built on the hashing keys ensures the scalability of our scheme and at the same time allows for real time reranking on query updates. Experiments in a publicly available benchmark dataset demonstrate the superiority of our approach. © 2012 Springer-Verlag.