text: 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 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- @math photos regarding the latent topics of a query landmark to construct multi-query set so as to remedy its possible . For this purpose, we significantly extend the techniques of Latent Dirichlet Allocation. Then, motivated by the typical methods, we propose to learn a 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 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.
  @cite_35: 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 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 shapes caused by the photography of q-users is often nontrivial and has never been studied. Motivated by this, 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 shape. For this purpose, we significantly extend the techniques of Latent Dirichlet Allocation. Secondly, we propose a novel technique to generate the robust yet compact pattern set from the multi-query photos. To ensure redundancy-free and enhance the efficiency, we adopt the existing minimum-description-length-principle based pattern mining techniques to remove similar query photos from the (k+1) selected query photos. Then, a landmark retrieval rule is developed to calculate the ranking scores between mined pattern set and each photo in the database, which are ranked to serve as the final ranking list of landmark retrieval. Extensive experiments are conducted on real-world landmark datasets, validating the significantly higher accuracy of our approach.
summary: These methods are commonly based on the idea that those particular multiple queries are manually selected or simply retrieved from top-k similar items whilst we automatically determine helpful queries by exploring the latent topics of query landmark as well as the informative user communities. Besides, previous query expansion pipelines are not applicable in the context of social media networks, which cannot be addressed by simple variations of methods in literature. To address the limitation, Wang al @cite_35 proposed a multi-query expansion technique to learn a robust mid-level pattern representation. Based on that, we propose to learn high-level deep feature over multi-query set, which is superior to the mid-level representation in @cite_35 .