Abstract
The increasing number of mobile phones equipped with powerful cameras leads to huge collections of user-generated
images. To utilize the information of the images on site, image retrieval systems are becoming more and more popular to
search for similar objects in an own image database. As the computational performance and the memory capacity of
mobile devices are constantly increasing, this search can often be performed on the device itself. This is feasible, for
example, if the images are represented with global image features or if the search is done using EXIF or textual
metadata. However, for larger image databases, if multiple users are meant to contribute to a growing image database or
if powerful content-based image retrieval methods with local features are required, a server-based image retrieval backend
is needed. In this work, we present a content-based image retrieval system with a client server architecture working
with local features. On the server side, the scalability to large image databases is addressed with the popular bag-of-word
model with state-of-the-art extensions. The client end of the system focuses on a lightweight user interface presenting the
most similar images of the database highlighting the visual information which is common with the query image.
Additionally, new images can be added to the database making it a powerful and interactive tool for mobile contentbased
image retrieval.
