Authors:
Anna Montesanto
;
Paola Baldassarri
;
A. F. Dragoni
;
G. Vallesi
and
P. Puliti
Affiliation:
DEIT, Università Politecnica delle Marche, Italy
Keyword(s):
Query by example, human action semantics, artificial intelligent systems, neural networks.
Related
Ontology
Subjects/Areas/Topics:
Human-Machine Interface
;
Multimedia
;
Multimedia Databases, Indexing, Recognition and Retrieval
;
Multimedia Systems and Applications
;
Telecommunications
Abstract:
The paper describes a method for extracting human action semantics in video’s using queries-by-example. 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.