In high dimension space, many conventional clustering algorithms do not work well in effectiveness and efficiency, especially for image data set. For example, k-means is widely used in image clustering especially visual clustering. But its drawback such as long clustering time and high memory cost seriously deteriorates feasibility in incremental large image set. To improve the feasibility, we ...

The way we represent intentions, behaviors, and the spatial context, is crucial for any approach to mobile intention recognition. Formal grammars are cognitively comprehensible and make expressiveness properties explicit. By adding spatial domain knowledge to a grammar we can reduce parsing ambiguities. We argue that there are a number of mobile intention recognition problems which require the ...