EMOD

Abstract

Recently, researchers have proposed solutions to build on-device mobile visual search (ODMVS) systems. Different from traditional client-server mobile visual search systems, an ODMVS supports image searching directly within a mobile device. An ODMVS needs to be designed with constrained hardware in mind e.g. limited memory, less powerful CPU. In this paper, we present, EMOD, an efficient on-device mobile visual search system based on the Bag-of-Visual-Word (BOVW) framework but uses a small visual dictionary. An Object Word Ranking (OWR) algorithm is proposed to efficiently identify the most useful visual words of an image so as to construct a compact image signature for fast retrieval and greatly improved retrieval performance. Due to having a small visual dictionary, we propose the Top Inverted Index Ranking scheme to reduce the number of candidate images for similarity calculation. In addition, EMOD adopts a more efficient version of the recently proposed Ranking Consistency re-ranking algorithm for further performance enhancement. Via extensive experimental evaluations, we demonstrate that our prototype EMOD system yields good retrieval accuracy and query response times for a database with over 10K images.