Name
Title | ||
|---|---|---|
Bayesian classification and active learning using lp-priors. Application to image segmentation |
Abstract | ||
|---|---|---|
In this paper we utilize Bayesian modeling and inference to learn a softmax classification model which performs Supervised Classification and Active Learning. For p <; 1, lp-priors are used to impose sparsity on the adaptive parameters. Using variational inference, all model parameters are estimated and the posterior probabilities of the classes given the samples are calculated. A relationship between the prior model used and the independent Gaussian prior model is provided. The posterior probabilities are used to classify new samples and to define two Active Learning methods to improve classifier performance: Minimum Probability and Maximum Entropy. In the experimental section the proposed Bayesian framework is applied to Image Segmentation problems on both synthetic and real datasets, showing higher accuracy than state-of-the-art approaches. |
Year | Venue | Keywords |
|---|---|---|
2014 | Signal Processing Conference | belief networks,image classification,image segmentation,learning (artificial intelligence),maximum entropy methods,probability,Bayesian classification,active learning method,image segmentation problem,independent Gaussian prior model,maximum entropy,minimum probability,posterior probabilities,softmax classification model,supervised classification,variational inference |
Field | DocType | Citations |
Active learning,Scale-space segmentation,Naive Bayes classifier,Pattern recognition,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Prior probability,Machine learning,Mathematics | Conference | 0 |
PageRank | References | Authors |
0.34 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pablo Ruiz | 1 | 37 | 9.59 |
| Nicolas Perez de la Blanca | 2 | 314 | 33.17 |
| Rafael Molina | 3 | 1439 | 103.16 |
| Aggelos K. Katsaggelos | 4 | 3410 | 340.41 |