Accession Number:

ADA624258

Title:

Sparse Representation of Multimodality Sensing Databases for Data Mining and Retrieval

Descriptive Note:

Final rept. 1 May 2012-30 Jun 2015

Corporate Author:

MICHIGAN UNIV ANN ARBOR

Personal Author(s):

Report Date:

2015-04-09

Pagination or Media Count:

22.0

Abstract:

We propose to apply recently developed methods of sparse representation and dimensionality reduction to multimodality image and video databases. Our research will consist of three interconnected components 1 multimodality feature extraction from the database 2 informationtheoretic similarity measures for pairwise matching 3 hierarchical similarity-based clustering and database updating. Information-theoretic measures, sparse approximation and dimensionality reduction will play key roles in our work. They will allow us to reduce complexity, accelerate query matching times, improve specificity of the query matches, and incorporate robustness to noise and other distortions. Experimental validation will be performed by a combination of simulation and experiment on multimodality databases. As part of this proposal we propose to build a small scale experimental LADAREO video acquisition testbed.

Subject Categories:

  • Information Science
  • Statistics and Probability
  • Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE