Research Area 5: Task-Driven Coupled Dictionary Design for Heterogeneous Sensory Data
Abstract
The objective of this proposal is to create new methods of coupling multimodal data via sparsity for object classification. Unlike existing research on dictionary design based on single modality, this effort will design coupled discriminative dictionaries using unstructured multimodal data. Effort will create a pair of discriminative coupled dictionaries to capture latent shared information that exist across two different modalities. The latent information will be exploited to directly compare features by computing the sparse codes for the two modalities (i.e., thermal and visible face images of the same subject). A task-driven dictionary learning method will be designed to provide supervised discriminative dictionaries using the recently developed bi-level optimization techniques. Exploitation of nonlinear relationships between sensory data via kernel theory will be embedded in these algorithms.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 12, 2017
- Source ID
- W911NF1610083
Entities
People
- Nasser M. Nasrabadi
Organizations
- Army Contracting Command
- United States Army
- West Virginia University