Name
Abstract | ||
|---|---|---|
A general formulation of "Bayesian Adaptation" for generative and discriminative classification in the topic model framework is proposed. A generic topic-independent Gaussian mixture model, known as the background GMM, is learned using all available training data and adapted to the individual topics. In the generative framework, a Gaussian variant of the spatial pyramid model is used with a Bayes classifier. For the discriminative case, a novel predictive histogram representation for an image is presented. This builds upon the adapted topic model structure, using the individual class dictionaries and Bayesian weighting. The resulting histogram representation is evaluated for classification using a Support Vector Machine (SVM). A comparative evaluation of the proposed image models with the standard ones in the image classification literature is provided on three benchmark datasets. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/CVPR.2011.5995674 | CVPR |
Keywords | Field | DocType |
Bayes methods,Gaussian processes,image classification,image representation,support vector machines,Bayes classifier,Bayesian adaptation,Bayesian weighting,SVM,background GMM,class dictionaries,discriminative classification,generative classification,image classification,image representation,predictive histogram representation,spatial pyramid model,support vector machine,topic-independent Gaussian mixture model | Histogram,Pattern recognition,Computer science,Support vector machine,Artificial intelligence,Gaussian process,Contextual image classification,Discriminative model,Mixture model,Machine learning,Bayes classifier,Generative model | Conference |
Volume | Issue | ISSN |
2011 | 1 | 1063-6919 |
Citations | PageRank | References |
15 | 0.67 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Dixit | 1 | 26 | 3.24 |
| N Rasiwasia | 2 | 1173 | 34.61 |
| Nuno Vasconcelos | 3 | 5410 | 273.99 |