  High user interaction capability of mobile devices can help improve the
accuracy of mobile visual search systems. At query time, it is possible to
capture multiple views of an object from different viewing angles and at
different scales with the mobile device camera to obtain richer information
about the object compared to a single view and hence return more accurate
results. Motivated by this, we developed a mobile multi-view object image
search system, using a client-server architecture. Multi-view images of objects
acquired by the mobile clients are processed and local features are sent to the
server, which combines the query image representations with early/late fusion
methods based on bag-of-visual-words and sends back the query results. We
performed a comprehensive analysis of early and late fusion approaches using
various similarity functions, on an existing single view and a new multi-view
object image database. The experimental results show that multi-view search
provides significantly better retrieval accuracy compared to single view
search.
