Name
Abstract | ||
|---|---|---|
This paper presents a novel manifold learning algorithm for high-dimensional data sets. The scope of the application focuses on the problem of motion tracking in video sequences. The framework presented is twofold. First, it is assumed that the samples are time ordered, providing valuable information that is not presented in the current methodologies. Second, the manifold topology comprises multiple charts, which contrasts to the most current methods that assume one single chart, being overly restrictive. The proposed algorithm, Gaussian process multiple local models (GP–MLM), can deal with arbitrary manifold topology by decomposing the manifold into multiple local models that are probabilistic combined using Gaussian process regression. In addition, the paper presents a multiple filter architecture where standard filtering techniques are integrated within the GP–MLM. The proposed approach exhibits comparable performance of state-of-the-art trackers, namely multiple model data association and deep belief networks, and compares favorably with Gaussian process latent variable models. Extensive experiments are presented using real video data, including a publicly available database of lip sequences and left ventricle ultrasound images, in which the GP–MLM achieves state of the art results. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TIP.2014.2303652 | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
Keywords | Field | DocType |
video signal processing,gp-mlm,gaussian processes,video sequences,motion tracking,lip sequences,regression analysis,gaussian process latent variable models,manifold topology,real video data,gaussian process multiple local models,left ventricle ultrasound images,manifold learning algorithm,gaussian process regression,object tracking,tracking,high dimensional data sets,deep belief networks,multiple charts,tangent bundle,multiple dynamics,publicly available database,standard filtering,multiple nonlinear models,multiple filter architecture,multiple model data association,manifold learning | Computer vision,Pattern recognition,Deep belief network,Filter (signal processing),Manifold alignment,Video tracking,Artificial intelligence,Gaussian process,Probabilistic logic,Nonlinear dimensionality reduction,Mathematics,Manifold | Journal |
Volume | Issue | ISSN |
23 | 4 | 1941-0042 |
Citations | PageRank | References |
1 | 0.34 | 32 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jacinto C. Nascimento | 1 | 396 | 40.94 |
| Jorge G. Silva | 2 | 8 | 2.18 |
| Jorge S. Marques | 3 | 535 | 67.78 |
| João Miranda Lemos | 4 | 1 | 0.34 |