Search for: over-complete-dictionaries
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    A bayesian framework for sparse representation-based 3-d human pose estimation

    , Article IEEE Signal Processing Letters ; Vol. 21, issue. 3 , 2014 , pp. 297-300 ; ISSN: 10709908 Babagholami-Mohamadabadi, B ; Jourabloo, A ; Zarghami, A ; Kasaei, S ; Sharif University of Technology
    A Bayesian framework for 3-D human pose estimation from monocular images based on sparse representation (SR) is introduced. Our probabilistic approach aims at simultaneously learning two overcomplete dictionaries (one for the visual input space and the other for the pose space) with a shared sparse representation. Existing SR-based pose estimation approaches only offer a point estimation of the dictionary and the sparse codes. Therefore, they might be unreliable when the number of training examples is small. Our Bayesian framework estimates a posterior distribution for the sparse codes and the dictionaries from labeled training data. Hence, it is robust to overfitting on small-size training... 

    Learning overcomplete dictionaries based on atom-by-atom updating

    , Article IEEE Transactions on Signal Processing ; Volume 62, Issue 4 , 15 February , 2014 , Pages 883-891 ; ISSN: 1053587X Sadeghi, M ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
    A dictionary learning algorithm learns a set of atoms from some training signals in such a way that each signal can be approximated as a linear combination of only a few atoms. Most dictionary learning algorithms use a two-stage iterative procedure. The first stage is to sparsely approximate the training signals over the current dictionary. The second stage is the update of the dictionary. In this paper we develop some atom-by-atom dictionary learning algorithms, which update the atoms sequentially. Specifically, we propose an efficient alternative to the well-known K-SVD algorithm, and show by various experiments that the proposed algorithm is much faster than K-SVD while its results are... 

    Learning overcomplete dictionaries based on parallel atom-updating

    , Article IEEE International Workshop on Machine Learning for Signal Processing, MLSP ; 2013 ; 21610363 (ISSN) ; 9781479911806 (ISBN) Sadeghi, M ; Babaie-Zadeh, M ; Jutten, C ; IEEE Signal Processing Society ; Sharif University of Technology
    In this paper we propose a fast and efficient algorithm for learning overcomplete dictionaries. The proposed algorithm is indeed an alternative to the well-known K-Singular Value Decomposition (K-SVD) algorithm. The main drawback of K-SVD is its high computational load especially in high-dimensional problems. This is due to the fact that in the dictionary update stage of this algorithm an SVD is performed to update each column of the dictionary. Our proposed algorithm avoids performing SVD and instead uses a special form of alternating minimization. In this way, as our simulations on both synthetic and real data show, our algorithm outperforms K-SVD in both computational load and the quality... 

    Regularized low-coherence overcomplete dictionary learning for sparse signal decomposition

    , Article European Signal Processing Conference, 28 August 2016 through 2 September 2016 ; Volume 2016-November , 2016 , Pages 369-373 ; 22195491 (ISSN) ; 9780992862657 (ISBN) Sadeghi, M ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
    European Signal Processing Conference, EUSIPCO  2016
    This paper deals with learning an overcomplete set of atoms that have low mutual coherence. To this aim, we propose a new dictionary learning (DL) problem that enables a control on the amounts of the decomposition error and the mutual coherence of the atoms of the dictionary. Unlike existing methods, our new problem directly incorporates the mutual coherence term into the usual DL problem as a regularizer. We also propose an efficient algorithm to solve the new problem. Our new algorithm uses block coordinate descent, and updates the dictionary atom-by-atom, leading to closed-form solutions. We demonstrate the superiority of our new method over existing approaches in learning low-coherence... 

    Learning overcomplete dictionaries from markovian data

    , Article 10th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2018, 8 July 2018 through 11 July 2018 ; Volume 2018-July , 2018 , Pages 218-222 ; 2151870X (ISSN); 9781538647523 (ISBN) Akhavan, S ; Esmaeili, S ; Babaie Zadeh, M ; Soltanian Zadeh, H ; Sharif University of Technology
    IEEE Computer Society  2018
    We explore the dictionary learning problem for sparse representation when the signals are dependent. In this paper, a first-order Markovian model is considered for dependency of the signals, that has many applications especially in medical signals. It is shown that the considered dependency among the signals can degrade the performance of the existing dictionary learning algorithms. Hence, we propose a method using the Maximum Log-likelihood Estimator (MLE) and the Expectation Minimization (EM) algorithm to learn the dictionary from the signals generated under the first-order Markovian model. Simulation results show the efficiency of the proposed method in comparison with the... 

    On the error in phase transition computations for compressed sensing

    , Article IEEE Transactions on Information Theory ; Volume 65, Issue 10 , 2019 , Pages 6620-6632 ; 00189448 (ISSN) Daei, S ; Haddadi, F ; Amini, A ; Lotz, M ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2019
    Evaluating the statistical dimension is a common tool to determine the asymptotic phase transition in compressed sensing problems with Gaussian ensemble. Unfortunately, the exact evaluation of the statistical dimension is very difficult and it has become standard to replace it with an upper-bound. To ensure that this technique is suitable, [1] has introduced an upper-bound on the gap between the statistical dimension and its approximation. In this work, we first show that the error bound in [1] in some low-dimensional models such as total variation and ell _{1} analysis minimization becomes poorly large. Next, we develop a new error bound which significantly improves the estimation gap... 

    A new algorithm for learning overcomplete dictionaries

    , Article European Signal Processing Conference, Marrakech ; Sept , 2013 , Page(s): 1 - 4 ; 22195491 (ISSN) ; 9780992862602 (ISBN) Sadeghi, M ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
    European Signal Processing Conference, EUSIPCO  2013
    In this paper, we propose a new algorithm for learning overcomplete dictionaries. The proposed algorithm is actually a new approach for optimizing a recently proposed cost function for dictionary learning. This cost function is regularized with a term that encourages low similarity between different atoms. While the previous approach needs to run an iterative limited-memory BFGS (l-BFGS) algorithm at each iteration of another iterative algorithm, our approach uses a closedform formula. Experimental results on reconstruction of a true underlying dictionary and designing a sparsifying dictionary for a class of autoregressive signals show that our approach results in both better quality and... 

    Dictionary learning with low mutual coherence constraint

    , Article Neurocomputing ; Volume 407 , 2020 , Pages 163-174 Sadeghi, M ; Babaie Zadeh, M ; Sharif University of Technology
    Elsevier B.V  2020
    This paper presents efficient algorithms for learning low-coherence dictionaries. First, a new algorithm based on proximal methods is proposed to solve the dictionary learning (DL) problem regularized with the mutual coherence of dictionary. This is unlike the previous approaches that solve a regularized problem where an approximate incoherence promoting term, instead of the mutual coherence, is used to encourage low-coherency. Then, a new solver is proposed for constrained low-coherence DL problem, i.e., a DL problem with an explicit constraint on the mutual coherence of the dictionary. As opposed to current methods, which follow a suboptimal two-step approach, the new algorithm directly...