@conference {Montesanto2008356,
	title = {Capturing the human action semantics using a query-by-example},
	booktitle = {SIGMAP 2008 - Proceedings of the International Conference on Signal Processing and Multimedia Applications},
	year = {2008},
	note = {cited By 0},
	pages = {356-363},
	abstract = {The paper describes a method for extracting human action semantics in video{\textquoteright}s using queries-by-example.b Here we consider the indexing and the matching problems of content-based human motion data retrieval. The query formulation is based on trajectories that may be easily built or extracted by following relevant points on a video, by a novice user too. The so realized trajectories contain high value of action semantics. The semantic schema is built by splitting a trajectory in time ordered sub-sequences that contain the features of extracted points. This kind of semantic representation allows reducing the search space dimensionality and, being human-oriented, allows a selective recognition of actions that are very similar among them. A neural network system analyzes the video semantic similarity, using a two-layer architecture of multilayer perceptrons, which is able to learn the semantic schema of the actions and to recognize them.},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-55849129652\&partnerID=40\&md5=a8587b1b9833a9ccf64510cc75e426ab},
	author = {A. Montesanto and Paola Baldassarri and Dragoni, A.F. and Vallesi, G. and Paolo Puliti}
}
