Pan-sharpening, a method for constructing high resolution images from low resolution observations, has recently been explored from the perspective of compressed sensing and sparse representation theory. We present a new pansharpening algorithm that uses a Bayesian nonparametric dictionary learning model to give an underlying sparse representation for image reconstruction. In contrast to existin...

In this paper, we address the problem of dictionary learning for sparse representation. Considering the regularized form of the dictionary learning problem, we propose a method based on a homotopy approach, in which the regularization parameter is overall decreased along iterations. We estimate the value of the regularization parameter adaptively at each iteration based on the current value of ...

We consider the problem of distributed dictionary learning, where a set of nodes is required to collectively learn a common dictionary from noisy measurements. This approach may be useful in several contexts including sensor networks. Diffusion cooperation schemes have been proposed to solve the distributed linear regression problem. In this work we focus on a diffusion-based adaptive dictionar...

Dictionary learning algorithms for audio modeling typically learn a dictionary such that each time frame of the given sound source is approximately equal to a linear combination of the dictionary elements. Since audio is non-stationary data, learning a single dictionary to explain all time frames of the sound source might not be the best modeling strategy. We therefore recently proposed a techn...

The study aimed at investigating whether the retention of vocabulary acquired incidentally is dependent upon the amount of task-induced involvement. Immediate and delayed retention of twenty unfamiliar words was examined in three learning tasks( listening comprehension + group discussion, listening comprehension + dictionary checking + summary writing in L1, and listening comprehension + dictio...

Previous researches have demonstrated that the framework of dictionary learning with sparse coding, in which signals are decomposed as linear combinations of a few atoms of a learned dictionary, is well adept to reconstruction issues. This framework has also been used for discrimination tasks such as image classification. To achieve better performances of classification, experts develop several...

We present theoretical guarantees for an alternating minimization algorithm for the dictionary learning/sparse coding problem. The dictionary learning problem is to factorize vector samples y, y, . . . , y into an appropriate basis (dictionary) A∗ and sparse vectors x1∗, . . . , xn∗. Our algorithm is a simple alternating minimization procedure that switches between l1 minimization and gradient ...

Dictionary learning is a method to learn dictionary items adapted to data of a given distribution. It is shown that dictionary learned from data is more suited for vision task than universal dictionaries [4]. Traditionally, Vector Quantization (VQ), or using k-means to learn data cluster centroids, is a simple and popular method in the bag-of-features framework [5]. Recently, sparse coding is u...

Existing dictionary learning algorithms rely heavily on the assumption that the data points are vectors in some Euclidean space R, and the dictionary is learned from the input data using only the vector space structure of R. However, in many applications, features and data points often belong to some Riemannian manifold with its intrinsic metric structure that is potentially important and criti...

Many recent efforts have shown the effectiveness of dictionary learning methods in solving several computer vision problems. However, when designing dictionaries, training and testing domains may be different, due to different view points and illumination conditions. In this paper, we present a function learning framework for the task of transforming a dictionary learned from one visual domain ...