Seeking probability models for images, we employ a spectral approach where the images
are decomposed using bandpass filters and probability models are imposed on the filter …

This paper applies a robust statistical scheme to the problem of unsupervised learning of
high-dimensional data. We develop, analyze, and apply a new finite mixture model based …

For effective retrieval of visual information, statistical learning plays a pivotal role. Statistical
learning in such a context faces at least two major mathematical challenges: scarcity of …

In this paper, two-dimensional stochastic linear models are used in developing algorithms
for image analysis such as classification, segmentation, and object detection in images …

We present a discriminative latent variable model for classification problems in structured
domains where inputs can be represented by a graph of local observations. A hidden-state …

Human experts constitute pattern classes of natural objects based on their observed
appearance. Automatic systems for pattern recognition may be designed on a structural …

The similarity between objects is a fundamental element of many learning algorithms. Most
non-parametric methods take this similarity to be fixed, but much recent work has shown the …

This modern treatment of computer vision focuses on learning and inference in probabilistic
models as a unifying theme. It shows how to use training data to learn the relationships …

The symmetric positive desnite (SPD) matrices have been widely used in image and vision
problems. Recently there are growing interests in studying sparse representation (SR) of …

We describe an algorithm for automatically learning discriminative components of objects
with SVM classifiers. It is based on growing image parts by minimizing theoretical bounds on …