A Bayesian Model for Simultaneous Image Clustering, Annotation and Object Segmentation.

Published

Journal Article

A non-parametric Bayesian model is proposed for processing multiple images. The analysis employs image features and, when present, the words associated with accompanying annotations. The model clusters the images into classes, and each image is segmented into a set of objects, also allowing the opportunity to assign a word to each object (localized labeling). Each object is assumed to be represented as a heterogeneous mix of components, with this realized via mixture models linking image features to object types. The number of image classes, number of object types, and the characteristics of the object-feature mixture models are inferred nonparametrically. To constitute spatially contiguous objects, a new logistic stick-breaking process is developed. Inference is performed efficiently via variational Bayesian analysis, with example results presented on two image databases.

Full Text

Duke Authors

Cited Authors

  • Du, L; Ren, L; Dunson, DB; Carin, L

Published Date

  • January 2009

Published In

Volume / Issue

  • 2009 /

Start / End Page

  • 486 - 494

PubMed ID

  • 25360065

Pubmed Central ID

  • 25360065

International Standard Serial Number (ISSN)

  • 1049-5258

Language

  • eng