A clustering framework within the sparse modeling and dictionary learning setting is
introduced in this work. Instead of searching for the set of centroid that best fit the data, as in …

This paper targets fine-grained image categorization by learning a category-specific
dictionary for each category and a shared dictionary for all the categories. Such category …

Handbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image
and Video Processing shows you how robust subspace learning and tracking by …

A clustering framework within the sparse modeling and dictionary learning setting is
introduced in this work. Instead of searching for the set of centroid that best fit the data, as in …

We propose a class of sparse coding models that utilizes a Laplacian Scale Mixture (LSM)
prior to model dependencies among coefficients. Each coefficient is modeled as a Laplacian …

A method is presented for subpixel modeling, mapping, and classification in hyperspectral
imagery using learned block-structured discriminative dictionaries, where each block is …

Principal component analysis (PCA) is widely used for dimensionality reduction, with well-
documented merits in various applications involving high-dimensional data, including …

In this paper, the problem of dictionary learning and its analogy to source separation is
addressed. First, we extend the well-known method of K-SVD to incoherent K-SVD, to …

In semiconductor manufacturing, wafer quality control strongly relies on product monitoring
and physical metrology. However, the involved metrology operations, generally performed …

In this paper, we present a novel method for ship classification in synthetic aperture radar
(SAR) images. The proposed method consists of feature extraction and classifier training …