  Given a query photo issued by a user (q-user), the landmark retrieval is to
return a set of photos with their landmarks similar to those of the query,
while the existing studies on the landmark retrieval focus on exploiting
geometries of landmarks for similarity matches between candidate photos and a
query photo. We observe that the same landmarks provided by different users
over social media community may convey different geometry information depending
on the viewpoints and/or angles, and may subsequently yield very different
results. In fact, dealing with the landmarks with \illshapes caused by the
photography of q-users is often nontrivial and has seldom been studied. In this
paper we propose a novel framework, namely multi-query expansions, to retrieve
semantically robust landmarks by two steps. Firstly, we identify the top-$k$
photos regarding the latent topics of a query landmark to construct multi-query
set so as to remedy its possible \illshape. For this purpose, we significantly
extend the techniques of Latent Dirichlet Allocation. Then, motivated by the
typical \emph{collaborative filtering} methods, we propose to learn a
\emph{collaborative} deep networks based semantically, nonlinear and high-level
features over the latent factor for landmark photo as the training set, which
is formed by matrix factorization over \emph{collaborative} user-photo matrix
regarding the multi-query set. The learned deep network is further applied to
generate the features for all the other photos, meanwhile resulting into a
compact multi-query set within such space. Extensive experiments are conducted
on real-world social media data with both landmark photos together with their
user information to show the superior performance over the existing methods.
