The Information Processing and Algorithms Laboratory (iPAL) is directed by Prof. Vishal Monga. Graduate research in iPAL broadly encompasses signal and image processing theory and applications with a particular focus on capturing practical real-world constraints via convex optimization theory and algorithms.
Here, we address sparse signal recovery in a Bayesian framework where sparsity is enforced on reconstruction coefficients via probabilistic priors. In particular, we focus on the setup of Yen et al. who employ a variant of spike and slab prior to encourage sparsity.
Spike and Slab priors have been of much recent interest in signal processing as a means of inducing sparsity in Bayesian inference. Applications domains that benefit from the use of these priors include sparse recovery, regression and classification.
Despite the fact that different objects possess distinct class-specific features, they also usually share common patterns. This observation has been exploited partially in a recently proposed dictionary learning framework by separating the particularity and the commonality (COPAR).
M. Li and V. Monga, "Two fold video hashing with automatic synchronization", IEEE Transactions on Information Forensics and Security, volume 10, issue 8, pages 1727-1738, August 2015.[IEEE Xplore]
M. Li and V. Monga, "Compact Video Fingerprinting Via Structural Graphical Models", IEEE Transactions on Information Forensics and Security , Volume 8, Issue 11, pp. 1709-1721, November, 2013. [IEEE Xplore]
P. Vemulapalli, V. Monga and S. Brennan, "Robust Extrema Features for Time-Series Data Analysis", IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 35, issue 6, pages 1464-1479, June 2013.[IEEE Xplore]
M. Li, V. Monga, "Robust Video Hashing via Multilinear Subspace Projections", IEEE Transactions on Image Processing, vol. 21, issue 10, pages 4397-4409, Oct 2012. [IEEE Xplore]
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