In this paper, face detection problem is considered using the concepts of compressive sensing technique. This technique includes dictionary learning procedure and sparse coding method to represent the structural content of input images. In the proposed method, dictionaries are learned in such a way that the trained models have the least degree of coherence to each other. The novelty of the prop...

—In this work we propose a new deep learning tool – deep dictionary learning. Multi-level dictionaries are learnt in a greedy fashion – one layer at a time. This requires solving a simple (shallow) dictionary learning problem; the solution to this is well known. We apply the proposed technique on some benchmark deep learning datasets. We compare our results with other deep learning tools like s...

Title of dissertation: SPARSE DICTIONARY LEARNING AND DOMAIN ADAPTATION FOR FACE AND ACTION RECOGNITION Qiang Qiu, Doctor of Philosophy, 2013 Dissertation directed by: Professor Rama Chellappa Department of Computer Science New approaches for dictionary learning and domain adaptation are proposed for face and action recognition. We first present an approach for dictionary learning of action att...

Discriminative and Compact Dictionary Design for Hyperspectral Image Classification using Learning VQ Framework Report Title Sparse representation provides an efficient description for high-dimensional Hyperspectral Imagery (HSI) and also encodes discriminative information useful for classification. However, due to the large size of typical HSI images, the naive way to construct a dictionary wi...

Convolutional dictionary learning (CDL), the problem of estimating shift-invariant templates from data, is typically conducted in absence a prior/structure on templates. In data-scarce or low signal-to-noise ratio (SNR) regimes, learned overfit data and lack smoothness, which can affect predictive performance downstream tasks. To address this limitation, we propose GPCDL, convolutional framewor...

In this paper, we present a novel dictionary learning framework for data lying on the manifold of square root densities and apply it to the reconstruction of diffusion propagator (DP) fields given a multi-shell diffusion MRI data set. Unlike most of the existing dictionary learning algorithms which rely on the assumption that the data points are vectors in some Euclidean space, our dictionary l...