Image Annotation and Topic Extraction Using Super-Word Latent Dirichlet Allocation

Abstract

This research presents a multi-domain solution that uses text and images to iteratively improve automated information extraction. Stage I uses local text surrounding an embedded image to provide clues that help rank-order possible image annotations. These annotations are forwarded to Stage II, where the image annotations from Stage I are used as highly-relevant "super-words" to improve extraction of topics. The model probabilities from the super-words in Stage II are forwarded to Stage III where they are used to refine the automated image annotation developed in Stage I. All stages demonstrate improvement over existing equivalent algorithms in the literature.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2013
Accession Number
ADA583830

Entities

People

  • George E. Noel Iii

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Air Platforms
  • Biomedical
  • Energy and Power Technologies
  • Ground and Sea Platforms
  • Human Systems

DTIC Thesaurus Topics

  • Aircrafts
  • Bayesian Networks
  • Computational Science
  • Computers
  • Data Mining
  • Databases
  • Generative Models
  • Geography
  • Information Retrieval
  • Information Science
  • Machine Learning
  • Monte Carlo Method
  • Natural Language Processing
  • Operating Systems
  • Probabilistic Models
  • Web Browsers
  • Word Processors

Readers

  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)
  • Library and Information Science
  • Medical Imaging.

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval