In this work we consider a machine learning setting where data are represented as graphs. First, we derive a kernel function which evaluates the similarity between graphs, while capturing pair-wise constraints between graph nodes. Second, we apply it to the problem of classifying collective activities: on this respect we first represent groups of people located in a spatial neighborhood as graphs, and then train a multi-class classifier able to capture the behavior of the groups. We evaluate our approach on a benchmark dataset and report a comparative analysis with other state-of-art methods which highlights the benefits of our approach.
A spectral graph kernel and its application to collective activities classification
NOCETI, NICOLETTA;ODONE, FRANCESCA
2014-01-01
Abstract
In this work we consider a machine learning setting where data are represented as graphs. First, we derive a kernel function which evaluates the similarity between graphs, while capturing pair-wise constraints between graph nodes. Second, we apply it to the problem of classifying collective activities: on this respect we first represent groups of people located in a spatial neighborhood as graphs, and then train a multi-class classifier able to capture the behavior of the groups. We evaluate our approach on a benchmark dataset and report a comparative analysis with other state-of-art methods which highlights the benefits of our approach.
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