Weizhi Nie

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—In recent years, we have witnessed a flourishing of location-based social networks. A well-formed representation of location knowledge is desired to cater to the need of location sensing, browsing, navigation and querying. In this paper, we aim to study the semantics of point-of-interest (POI) by exploiting the abundant heterogeneous user generated content(More)
Human action recognition is one of the most active research areas in both computer vision and machine learning communities. Several methods for human action recognition have been proposed in the literature and promising results have been achieved on the popular datasets. However, the comparison of existing methods is often limited given the different(More)
With the rapid development of location-based social networks (LB-SNs), multimedia topic modeling on location-related user generated contents (UGCs) for venues is strongly desired. However, most of the previous topic mod-eling approaches only work on single modality data, or correlated multimodal data. The intrinsic property of UGCs in LBSNs that the(More)
This paper originally proposes the clique-graph and further presents a clique-graph matching method by preserving global and local structures. Especially, we formulate the objective function of clique-graph matching with respective to two latent variables, the clique information in the original graph and the pairwise clique correspondence constrained by the(More)
Multi-view matching is an important but a challenging task in view-based 3D model retrieval. To address this challenge, we propose an original multi-modal clique graph (MCG) matching method in this paper. We systematically present a method for MCG generation that is composed of cliques, which consist of neighbor nodes in multi-modal feature space and(More)