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Nested dictionary learning for hierarchical organization of imagery and text

dc.contributor.author Carin, Lawrence
dc.contributor.author Li, L
dc.contributor.author Zhang, XX
dc.contributor.author Zhou, M
dc.date.accessioned 2014-07-22T16:12:59Z
dc.date.issued 2012-12-01
dc.identifier.uri https://hdl.handle.net/10161/8947
dc.description.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.
dc.relation.ispartof Uncertainty in Artificial Intelligence - Proceedings of the 28th Conference, UAI 2012
dc.title Nested dictionary learning for hierarchical organization of imagery and text
dc.type Journal article
pubs.begin-page 469
pubs.end-page 478
pubs.organisational-group Duke
pubs.organisational-group Electrical and Computer Engineering
pubs.organisational-group Pratt School of Engineering
pubs.publication-status Published


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