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In this paper, we propose a crowd motion partitioning approach based on local-translational motion approximation in a scattered motion field. To represent crowd motion in an accurate and parsimonious way, we compute optical flow at the salient locations instead of at all the pixel locations. We then transform the problem of crowd motion partitioning into a problem of scattered motion field segmentation. Based on our assumption that local crowd motion can be approximated by a translational motion field, we develop a local-translation domain segmentation (LTDS) model in which the evolution of domain boundaries is derived from the Gâteaux derivative of an objective functional and further extend LTDS to the case of scattered motion field. The experiment results on a set of synthetic vector fields and a set of videos depicting real-world crowd scenes indicate that the proposed approach is effective in identifying the homogeneous crowd motion components under different scenarios. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a novel 3-D model retrieval framework, which is referred to as hybrid 3-D model associative retrieval. Unlike the conventional 3-D model similarity retrieval approach, the query model and the models obtained by 3-D model hybrid associative retrieval have the following properties: They belong to different model classes and have different shape characteristics in general but are semantically related and preassembled in a certain associative group. For instance, given a furniture associative group \ \desk, \chair, \bed\, we may probably like to use a desk as a query model to search for a list of matching models, which belong to the chair or bed class. We consider the following possibilities: 1) there can be more than two classes in an association group and 2) different association groups might have different numbers of classes. The hybrid associative retrieval is performed in two stages: 1) to establish the relationship between different 3-D model categories with semantic associations, we propose three approaches based on neural network learning and 2) to address the aforementioned two conditions, we use a cyclic-shift scheme to partition different associative groups into two-class pairwise associative groups and then adopt two different strategies to combine the final retrieval results. Experiments by using different data sets demonstrate the effectiveness and efficiency of our proposed framework on the new hybrid associative retrieval task. [ABSTRACT FROM AUTHOR]