Probabilistic models for supervised dictionary learning
| Autor: | Bao-Liang Lu, Zhiwei Li, Lei Zhang, Changhu Wang, Xiaochen Lian |
|---|---|
| Rok vydání: | 2010 |
| Předmět: |
K-SVD
Contextual image classification business.industry Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Probabilistic logic Statistical model Pattern recognition Mixture model Logistic regression Machine learning computer.software_genre ComputingMethodologies_PATTERNRECOGNITION Discriminative model Categorization Computer Science::Computer Vision and Pattern Recognition Histogram Pyramid Pyramid (image processing) Artificial intelligence business computer |
| Zdroj: | CVPR |
| DOI: | 10.1109/cvpr.2010.5539915 |
| Popis: | Dictionary generation is a core technique of the bag-of-visual-words (BOV) models when applied to image categorization. Most of previous approaches generate dictionaries by unsupervised clustering techniques, e.g. k-means. However, the features obtained by such kind of dictionaries may not be optimal for image classification. In this paper, we propose a probabilistic model for supervised dictionary learning (SDLM) which seamlessly combines an unsuper-vised model (a Gaussian Mixture Model) and a supervised model (a logistic regression model) in a probabilistic framework. In the model, image category information directly affects the generation of a dictionary. A dictionary obtained by this approach is a trade-off between minimization of distortions of clusters and maximization of discriminative power of image-wise representations, i.e. histogram representations of images. We further extend the model to incorporate spatial information during the dictionary learning process in a spatial pyramid matching like manner. We extensively evaluated the two models on various benchmark dataset and obtained promising results. |
| Databáze: | OpenAIRE |
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