Compression of facial images using the K-SVD algorithm

Bryt, Ori; Elad, Michael

doi:10.1016/j.jvcir.2008.03.001

Cited by 281 publications

(203 citation statements)

References 24 publications

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“…In recent years, sparse representation has gained much interest in the computer vision field [1,2] and has been widely applied to image restoration [3,4], image compression [5,6], and image classification [7][8][9][10][11]. The success of sparse representation is partially because natural images can be generally and sparsely coded by structural primitives (e.g., edges and line segments) and the images or signals can be represented sparsely by dictionary atoms from the same class.…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised dictionary learning with label propagation for image classification

Chen

Yang

2017

Comp. Visual Media

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Sparse coding and supervised dictionary learning have rapidly developed in recent years, and achieved impressive performance in image classification. However, there is usually a limited number of labeled training samples and a huge amount of unlabeled data in practical image classification, which degrades the discrimination of the learned dictionary. How to effectively utilize unlabeled training data and explore the information hidden in unlabeled data has drawn much attention of researchers. In this paper, we propose a novel discriminative semisupervised dictionary learning method using label propagation (SSD-LP). Specifically, we utilize a label propagation algorithm based on class-specific reconstruction errors to accurately estimate the identities of unlabeled training samples, and develop an algorithm for optimizing the discriminative dictionary and discriminative coding vectors simultaneously. Extensive experiments on face recognition, digit recognition, and texture classification demonstrate the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Semi-supervised dictionary learning with label propagation for image classification

Chen

Yang

2017

Comp. Visual Media

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“…In particular, sparsity based regularization has achieved great success, offering solutions that outperform classical approaches in various image and signal processing applications. Among the others, we can mention inverse problems such as denoising [35,36], reconstruction [22,37], classification [38], recognition [39,40], and compression [41,42]. The underlying assumption of methods based on sparse representation is that signals such as audio and images are naturally generated by a multivariate linear model, driven by a small number of basis or regressors.…”

Section: Sparse Image Representationmentioning

confidence: 99%

Non-Local Sparse Image Inpainting for Document Bleed-Through Removal

et al. 2018

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Bleed-through is a frequent, pervasive degradation in ancient manuscripts, which is caused by ink seeped from the opposite side of the sheet. Bleed-through, appearing as an extra interfering text, hinders document readability and makes it difficult to decipher the information contents. Digital image restoration techniques have been successfully employed to remove or significantly reduce this distortion. This paper proposes a two-step restoration method for documents affected by bleed-through, exploiting information from the recto and verso images. First, the bleed-through pixels are identified, based on a non-stationary, linear model of the two texts overlapped in the recto-verso pair. In the second step, a dictionary learning-based sparse image inpainting technique, with non-local patch grouping, is used to reconstruct the bleed-through-contaminated image information. An overcomplete sparse dictionary is learned from the bleed-through-free image patches, which is then used to estimate a befitting fill-in for the identified bleed-through pixels. The non-local patch similarity is employed in the sparse reconstruction of each patch, to enforce the local similarity. Thanks to the intrinsic image sparsity and non-local patch similarity, the natural texture of the background is well reproduced in the bleed-through areas, and even a possible overestimation of the bleed through pixels is effectively corrected, so that the original appearance of the document is preserved. We evaluate the performance of the proposed method on the images of a popular database of ancient documents, and the results validate the performance of the proposed method compared to the state of the art.

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“…Some available methods of texture classification and retrieval can be found in [1,2,3,4]. SVD is an important matrix theory and has been popularly employed in image processing, such as data compression [5], texture segmentation [6], and texture classification [7]. Specifically, wavelet-based methods have been intensively researched since wavelet analysis offers an efficient representation of multiresolutions and orientations of images [8,9,10,11].…”

Section: Introductionmentioning

confidence: 99%

Texture Classification by Bit-plane Multifractal Spectrum and Bit-plane Barycentric Coordinates of Wavelet Coefficients Based on SVD

Wang¹,

Wang²

2017

dtcse

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A new texture classification method based on singular value decomposition(SVD) and wavelet transform is presented. Wavelet transform is employed on texture images having been preprocessed with SVD. The elements of the signature vector of an image are the fractal dimensions and barycentric coordinates of the bit planes of the wavelet coefficients in both the 3-Level high frequency domains and the third low frequency domain. The one-nearest-neighbor classifier with standard L ଵ -norm distance is utilized to perform supervised texture classification. Compared with some other classification methods, the method is experimentally proved more efficient and less time-consuming.

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Compression of facial images using the K-SVD algorithm

Cited by 281 publications

References 24 publications

Semi-supervised dictionary learning with label propagation for image classification

Semi-supervised dictionary learning with label propagation for image classification

Non-Local Sparse Image Inpainting for Document Bleed-Through Removal

Texture Classification by Bit-plane Multifractal Spectrum and Bit-plane Barycentric Coordinates of Wavelet Coefficients Based on SVD

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