Paper Info
Title
Information geometric algorithm for estimating switching probabilities in space-varying HMM.
Abstract
This paper proposes an iterative natural gradient algorithm to perform the optimization of switching probabilities in a space-varying hidden Markov model, in the context of human activity recognition in long-range surveillance. The proposed method is a version of the gradient method, developed under an information geometric viewpoint, where the usual Euclidean metric is replaced by a Riemannian metric on the space of transition probabilities. It is shown that the change in metric provides advantages over more traditional approaches, namely: 1) it turns the original constrained optimization into an unconstrained optimization problem; 2) the optimization behaves asymptotically as a Newton method and yields faster convergence than other methods for the same computational complexity; and 3) the natural gradient vector is an actual contravariant vector on the space of probability distributions for which an interpretation as the steepest descent direction is formally correct. Experiments on synthetic and real-world problems, focused on human activity recognition in long-range surveillance settings, show that the proposed methodology compares favorably with the state-of-the-art algorithms developed for the same purpose.
Year
DOI
Venue
2014
10.1109/TIP.2014.2363614
IEEE Transactions on Image Processing
Keywords
Field
DocType
hidden markov models,trajectory,vectors,switches,measurement
Gradient method,Gradient descent,Mathematical optimization,Forward algorithm,Markov model,Euclidean distance,Algorithm,Newton's method in optimization,Probability distribution,Optimization problem,Mathematics
Journal
Volume
Issue
ISSN
23
12
1057-7149
Citations 
PageRank 
References 
0
0.34
20
Authors
4
Name
Order
Citations
PageRank
Jacinto C. Nascimento139640.94
miguel barao2112.40
Jorge S. Marques353567.78
João Miranda Lemos45410.64