Distributed classification of multiple observations by consensus

We consider the problem of distributed classification of multiple observations of the same object that are collected in an ad-hoc network of vision sensors. Assuming that each sensor captures a different observation of the same object, the problem is to classify this object by distributed processing from the sensors. We present a graphbased problem formulation whose objective function captures the smoothness of candidate labels on the data manifold. We design a distributed average consensus algorithm for estimating the unknown object class by computing the value of the above smoothness objective function for different class hypotheses. It initially estimates the objective function locally, based on the observation of each sensor. All the observations are then progressively taken into account in the estimation of the objective function, along the iterations of the distributed consensus algorithm. We illustrate the performance of the distributed classification algorithm by simulation of...

• Type: Journal
• Year: 2010
• Where: ICIP
• Authors: Effrosini Kokiopoulou, Pascal Frossard
• Keywords: Ad-hoc Network, ICIP 2010, Image Processing, Objective Function, Sensors