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Traditional approaches in object class recognition utilize a large number of labeled visual examples in order to train classifiers to recognize the category of an object in a test image. However, the need for a large number of training data makes the scalability of this approach problematic. In this paper, we explore the recently proposed paradigm of attribute based category recognition for object...
Data integration is the problem of combining data residing at different sources, and providing the user with a unified view of these data. One of the critical issues of data integration is the detection of similar entities based on the content. This complexity is due to three factors: the data type of the databases are heterogeneous, the schema of databases are unfamiliar and heterogenous as well,...
In this paper, we construct a neural-inspired computational model based on the representational capabilities of receptive fields. The proposed model, known as shape encoding receptive fields (SERF), is able to perform fast and accurate data classification and regression of multi-dimensional data. A SERF is a histogram structure that encodes the shape of multi-dimensional data relative to its center,...
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