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Recently, attributes have been introduced to help object classification. Multi-task learning is an effective methodology to achieve this goal, which shares low-level features between attribute and object classifiers. Yet such a method neglects the constraints that attributes impose on classes which may fail to constrain the semantic relationship between the attribute and object classifiers. In this...
In the literature of human action recognition, despite promising results have been obtained by the traditional bag-of-words model, the relationship among spatiotemporal points has rarely been considered. Furthermore, serious quantization error also exists in this kind of strategy. In this paper, we propose a novel coding strategy named contextual Fisher kernels to overcome these limitations. We add...
Although traditional bag-of-words model has shown promising results for action recognition, it takes no consideration of the relationship among spatio–temporal points; furthermore, it also suffers serious quantization error. In this letter, we propose a novel coding strategy called context-constrained linear coding (CLC) to overcome these limitations. We first calculate the contextual distance between...
The service system for secondary business incubation of high-tech industry, the theme discussed in this paper, is to provide different kinds of service to support to cultivate and develop high-tech industry in terms of secondary business incubation, and consisted of such three stages as enterprises entering incubator service, in incubator service and exiting from incubator service. Different service...
Outlier mining is an important part in data mining. In this paper, through the research of causes and detection methods of outlier, combined with the perspective of granular computing, we firstly present a general guiding principle of outlier mining that is granulation viewpoint, which shows that choosing reasonable granularity before granulation plays a very crucial role in outlier mining. Then we...
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