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Spatial arrangements of people in a photo reflect how people regard themselves in a group. Comparing spatial arrangements of subjects in two group photos can help computer understand similar social semantics and events. In this paper, we incorporate gender information with spatial arrangements of subjects to assess the visual similarity between two group photos. For each group photo, detected faces...
Trajectories of foreground objects provide rich and continuous motion information for event analysis in surveillance videos. These trajectories can be obtained by tracking foreground objects frame by frame. When appearances of foreground objects significantly change during occlusions, tracking results may become incorrect. To solve the occlusion problem, we propose a real-time post processing method...
In this paper, we propose a vision based campus guide system to provide visitors guide information on the campus. Images of buildings are firstly represented by visual words. Then, to further reduce the recognition time, visual words of the same building are merged to a distinct codebook. During visiting, visitors capture images of an interested building by their mobile phones for recognition. After...
In this paper, we propose a novel orientation-aware Urquhart graph based spatial face context representation method to efficiently describe the spatial relationship among faces in group photos. We combine graph matching with orientations of graph edges to assess the similarity of spatial face contexts from different group photos. The experimental results show that our method can find more structurally...
Many works in computer vision attempt to solve different tasks such as object detection, scene recognition or attribute detection, either separately or as a joint problem. In recent years, there has been a growing interest in combining the results from these different tasks in order to provide a textual description of the scene. However, when describing a scene, there are many items that can be mentioned...
Current graph embedding frameworks of supervised dimensionality reduction often preserve the intraclass local structures and maximize the interclass variance. However, this strategy fails to provide adequate results when strict within-class multimodalities contradict between-class separations. In this paper, we propose Hypersphere Distribution Discriminant Analysis (HDDA), which determines the affinity...
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