Nested dictionary learning for hierarchical organization of imagery and text

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Li, L; Zhang, XX; Zhou, M; Carin, L

Published Date

  • December 1, 2012

Published In

  • Uncertainty in Artificial Intelligence Proceedings of the 28th Conference, Uai 2012

Start / End Page

  • 469 - 478

Citation Source

  • Scopus