Class specific sparse representation for classification

Huang, Sheng; Yu, Yang; Yang, Dan; Huangfu, Luwen; Zhang, Xiaohong

doi:10.1016/j.sigpro.2015.04.018

Cited by 64 publications

(46 citation statements)

References 25 publications

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“…Furthermore, LAST outperforms the unsupervised sparse coding classifier in both datasets. Interestingly, the proposed scheme also competes with, and sometimes outperforms the discriminative sparse coding techniques of (Huang and Aviyente, 2006;Mairal et al, 2008;Ramirez et al, 2010), where the dictionary is tuned for classification. While providing comparable results, the LAST classifier is much faster at test time than sparse coding techniques and RBF-SVM classifiers.…”

Section: Handwritten Digits Classificationmentioning

confidence: 96%

“…We compare LAST to baseline classification techniques described in the previous section, as well as to sparse coding based methods. In addition to building the dictionary in an unsupervised way, we consider the sparse coding classifiers in Mairal et al (2008); Huang and Aviyente (2006); Ramirez et al (2010), which construct the dictionary in a supervised fashion.…”

Section: Handwritten Digits Classificationmentioning

confidence: 99%

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Dictionary Learning for Fast Classification Based on Soft-thresholding

2014

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Classifiers based on sparse representations have recently been shown to provide excellent results in many visual recognition and classification tasks. However, the high cost of computing sparse representations at test time is a major obstacle that limits the applicability of these methods in large-scale problems, or in scenarios where computational power is restricted. We consider in this paper a simple yet efficient alternative to sparse coding for feature extraction. We study a classification scheme that applies the soft-thresholding nonlinear mapping in a dictionary, followed by a linear classifier. A novel supervised dictionary learning algorithm tailored for this low complexity classification architecture is proposed. The dictionary learning problem, which jointly learns the dictionary and linear classifier, is cast as a difference of convex (DC) program and solved efficiently with an iterative DC solver. We conduct experiments on several datasets, and show that our learning algorithm that leverages the structure of the classification problem outperforms generic learning procedures. Our simple classifier based on soft-thresholding also competes with the recent sparse coding classifiers, when the dictionary is learned appropriately. The adopted classification scheme further requires less computational time at the testing stage, compared to other classifiers. The proposed scheme shows the potential of the adequately trained soft-thresholding mapping for classification and paves the way towards the development of very efficient classification methods for vision problems.

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Section: Handwritten Digits Classificationmentioning

confidence: 96%

Section: Handwritten Digits Classificationmentioning

confidence: 99%

Dictionary Learning for Fast Classification Based on Soft-thresholding

2014

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“…(24) and the problem of sparse signal estimation in compressed sensing, Eq. (28), both have the same mathematical form [11,22],…”

Section: Obtaining Sparse Solutions For Signal Representation and Sigmentioning

confidence: 99%

Sparsity in Bayesian Signal Estimation

Wickramasingha¹,

Sobhy²,

Sherif³

2017

Bayesian Inference

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In this chapter, we describe different methods to estimate an unknown signal from its linear measurements. We focus on the underdetermined case where the number of measurements is less than the dimension of the unknown signal. We introduce the concept of signal sparsity and describe how it could be used as prior information for either regularized least squares or Bayesian signal estimation. We discuss compressed sensing and sparse signal representation as examples where these sparse signal estimation methods could be applied.

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“…Sparse representation focuses on finding the most compact representation of a given signal [1]. Amongst them, the K-SVD is one way of designing overcomplete dictionaries to achieve sparse data representation [2].…”

Section: Introductionmentioning

confidence: 99%

Image coding with incomplete transform competition for HEVC

Arrufat

Perrin

Philippe

2015

2015 IEEE International Conference on Image Processing (ICIP)

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Overcomplete transforms have received considerable attention over the past years. However, they often suffer from a complexity burden. In this paper, a low complexity approach is provided, where an orthonormal basis is complemented with a set of incomplete transforms: those incomplete transforms include a reduced number of basis vectors that allow a reduction on the coding complexity and ensure a certain level of sparsity. The solution has been implemented in the HEVC standard and compression gains of around 1% on average are reported while reducing the decoder complexity in about 5%.

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Class specific sparse representation for classification

Cited by 64 publications

References 25 publications

Dictionary Learning for Fast Classification Based on Soft-thresholding

Dictionary Learning for Fast Classification Based on Soft-thresholding

Sparsity in Bayesian Signal Estimation

Image coding with incomplete transform competition for HEVC

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