NIPS 2008 paper

A "Shape Aware" Model for semi-supervised Learning of Objects and its Context

Integrating semantic and syntactic analysis is essential for document analysis. Using an analogous reasoning, we present an approach that combines bag-of-words and spatial models to perform semantic and syntactic analysis for recognition of an object based on its internal appearance and its context. We argue that while object recognition requires modeling relative spatial locations of image features within the object, a bag-of-word is sufficient for representing context. Learning such a model from weakly labeled data involves labeling of features into two classes: foreground(object) or ''informative'' background(context). labeling. We present a ''shape-aware'' model which utilizes contour information for efficient and accurate labeling of features in the image. Our approach iterates between an MCMC-based labeling and contour based labeling of features to integrate co-occurrence of features and shape similarity.

http://www.umiacs.umd.edu/~agupta/

SDL: Supervised Dictionary Learning

It is now well established that sparse signal models are well suited to restoration tasks and can effectively be learned from audio, image, and video data. Recent research has been aimed at learning discriminative sparse models instead of purely reconstructive ones. This paper proposes a new step in that direction with a novel sparse representation for signals belonging to different classes in terms of a shared dictionary and multiple decision functions. It is shown that the linear variant of the model admits a simple probabilistic interpretation, and that its most general variant also admits a simple interpretation in terms of kernels. An optimization framework for learning all the components of the proposed model is presented, along with experiments on standard handwritten digit and texture classification tasks.

Learning Taxonomies(分类系统) by Dependence Maximization
We introduce a family of unsupervised algorithms, numerical taxonomy clustering,
to simultaneously cluster data, and to learn a taxonomy that encodes the relationship
between the clusters. The algorithms work by maximizing the dependence
between the taxonomy and the original data. The resulting taxonomy is
a more informative visualization of complex data than simple clustering; in addition,
taking into account the relations between different clusters is shown to
substantially improve the quality of the clustering, when compared with state-ofthe-
art algorithms in the literature (both spectral clustering and a previous dependence
maximization approach). We demonstrate our algorithm on image and text
data.