Nested dictionary learning for hierarchical organization of imagery and text
Abstract
A tree-based dictionary learning model is developed for joint analysis of imagery
and associated text. The dictionary learning may be applied directly to the imagery
from patches, or to general feature vectors extracted from patches or superpixels
(using any existing method for image feature extraction). Each image is associated
with a path through the tree (from root to a leaf), and each of the multiple patches
in a given image is associated with one node in that path. Nodes near the tree root
are shared between multiple paths, representing image characteristics that are common
among different types of images. Moving toward the leaves, nodes become specialized,
representing details in image classes. If available, words (text) are also jointly
modeled, with a path-dependent probability over words. The tree structure is inferred
via a nested Dirichlet process, and a retrospective stick-breaking sampler is used
to infer the tree depth and width.
Type
Journal articlePermalink
https://hdl.handle.net/10161/8947Collections
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Show full item recordScholars@Duke
Lawrence Carin
Professor of Electrical and Computer Engineering
Lawrence Carin earned the BS, MS, and PhD degrees in electrical engineering at the
University of Maryland, College Park, in 1985, 1986, and 1989, respectively. In 1989
he joined the Electrical Engineering Department at Polytechnic University (Brooklyn)
as an Assistant Professor, and became an Associate Professor there in 1994. In September
1995 he joined the Electrical and Computer Engineering (ECE) Department at Duke University,
where he is now a Professor. He was ECE Department Chair from 2011

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