Name
Abstract | ||
|---|---|---|
Abstract We present a framework,to discover and characterize different classes of everyday activities from event-streams. We begin by representing activities as bags of event n-grams. This allows us to analyze the global structural information of activities, using their local event statistics. We demonstrate how maximal cliques in an undirected edge-weighted graph of activities, can be used for activity-class discovery in an unsupervised manner. We show,how,modeling an activity as a variable length Markov process, can be used to discover recurrent event-motifs to characterize the discovered activity-classes. We present results over extensive data-sets, collected from multiple active environments, to show the competence,and generalizability of our proposed framework. |
Year | Venue | Keywords |
|---|---|---|
2005 | Uncertainty in Artificial Intelligence | markov process |
DocType | Volume | Citations |
Conference | abs/1207.1381 | 3 |
PageRank | References | Authors |
0.55 | 14 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rafay Hammid | 1 | 3 | 0.55 |
| Siddhartha Maddi | 2 | 67 | 3.56 |
| Amos Y. Johnson | 3 | 288 | 42.58 |
| Aaron F. Bobick | 4 | 6019 | 727.83 |
| Irfan A. Essa | 5 | 4876 | 580.85 |
| Charles L. Isbell | 6 | 504 | 65.79 |