Name
Abstract | ||
|---|---|---|
This paper addresses the problem of recovering 3D human pose from a single monocular image. In the literature, Bayesian Mixtures of Experts (BME) was successfully used to represent the multimodal image-to-pose distributions. However, the expectation-maximization (EM) algorithm that learns the BME model may converge to a suboptimal local maximum. And the quality of the final solution depends largely on the initial values. In this paper, we propose an efficient initialization method for BME learning. We first partition the training set so that each subset can be well modeled by a single expert and the total regression error is minimized. Then each expert and gate of BME model is initialized on a partition subset. Our initialization method is tested on both a quasi-synthetic dataset and a real dataset (HumanEva). Results show that it greatly reduces the computational cost in training while improves testing accuracy. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICIP.2008.4712217 | ICIP |
Keywords | Field | DocType |
human pose estimation,bayesian mixtures of ex- perts,index terms— initialization,initialization,bayesian mixtures of experts,em algorithm,robustness,kernel,bayesian methods,testing,pose estimation,indexing terms | Kernel (linear algebra),Pattern recognition,Regression,Computer science,Pose,Robustness (computer science),Artificial intelligence,Initialization,Partition (number theory),AND gate,Machine learning,Bayesian probability | Conference |
ISSN | ISBN | Citations |
1522-4880 E-ISBN : 978-1-4244-1764-3 | 978-1-4244-1764-3 | 2 |
PageRank | References | Authors |
0.44 | 13 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| huazhong ning | 1 | 1015 | 51.85 |
| Yuxiao Hu | 2 | 2209 | 103.06 |
| Thomas S. Huang | 3 | 27815 | 2618.42 |