Statistical Relational Learning (SRL) as an Enabling Technology for Data Acquisition and Data Fusion in Video

Abstract

While our ability to gather vast amounts of video data is growing at a staggering rate, our ability to effectively store, process, and analyze this video has not kept pace. It is therefore necessary to develop automatic methods for allocating limited resources in video understanding. It particular, it is important to reason about which portions of video require expensive analysis and storage. This project aims to make these inferences using new and existing tools from Statistical Relational Learning (SRL). SRL is a recently emerging technology that enables the effective integration of statistical or probabilistic information, with relational or logical domain information, providing the ability to reason collectively about large, complex, interacting domains.

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Document Details

Document Type
Technical Report
Publication Date
May 02, 2013
Accession Number
ADA585576

Entities

People

  • David R. Jacobs
  • Lise Getoor

Organizations

  • University of Maryland

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Acquisition
  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Computer Science
  • Computer Vision
  • Data Acquisition
  • Data Fusion
  • Data Mining
  • Detection
  • Emerging Technology
  • Engineering
  • Machine Learning
  • Object Recognition
  • Recognition
  • Social Media
  • Students

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence
  • Distributed Systems and Data Platform Development

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy