EMOVIS: An Efficient Mobile Visual Search System for Landmark Recognition

Abstract

Traditionally, content-based image retrieval systems (CBIR) are designed to allow users to search for images in large databases which match closely with users' query images. Recent emergence of powerful mobile devices equipped with digital cameras have led to the emergence of several interesting mobile CBIR applications. Due to the limited resources in mobile devices, it is critical that the image matching engine within any mobile CBIR system be efficiently designed. Many existing image matching engines use SURF-based methods which return many key points, and hence are not quite suitable for mobile devices. In this paper, we present an efficient mobile visual search system (EMOVIS) which allows mobile users to retrieve relevant information using image-based queries. EMOVIS uses two unique salient key point identification schemes we designed which allow image matching to be conducted efficiently and with high accuracy. In addition, EMOVIS includes an image cropping scheme which eliminates irrelevant regions within a query image. Such cropping minimizes query latency, bandwidth usage and the energy cost of using EMOVIS. Via extensive evaluations using ZuBuD dataset and our own image dataset, we showed that EMOVIS can achieve higher than 92% accuracy with low computational and energy cost.