Probabilistic Models for Supervised Dictionary Learning

Dictionary generation is a core technique of the bag-of-
visual-words (BOV) models when applied to image cate-
gorization. Most of previous approaches generate dictio-
naries 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 frame-
work. In the model, image category information directly
affects the generation of a dictionary. A dictionary ob-
tained 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 repre-
sentations of images. We further extend the model to incor-
porate spatial information during the dictionary learning
process in a spatial pyramid matching like manner. We ex-
tensively evaluated the two models on various benchmark
dataset and obtained promising results.