This paper addresses the problem of image representation based on a sparse decomposition over a learned dictionary. We propose an improved matching pursuit algorithm for Multiple Measurement Vectors (MMV) and an adaptive algorithm for dictionary learning based on multi-Singular Value Decomposition (SVD), and combine them for image representation. Compared with the traditional K-SVD and orthogon...

Synthetic aperture radar (SAR) image change detection is still a challenge due to inherent speckle noise and scarce datasets. This article proposes joint-related dictionary learning algorithm based on the k-singular value decomposition (K-SVD) called JR-KSVD an iterative adaptive threshold optimization (IATO) for unsupervised detection. The adds correlation K-SVD generate uniform initial dual-t...

The discrete empirical interpolation method (DEIM) may be used as an index selection strategy for formulating a CUR factorization. A notable drawback of the original DEIM algorithm is that number column or row indices can selected limited to input singular vectors. We propose new variant DEIM, which we call L-DEIM, combination strength deterministic leverage scores and DEIM. This allows greater...

Traditional dictionary learning methods are based on quadratic convex loss function and thus are sensitive to outliers. In this paper, we propose a generic framework for robust dictionary learning based on concave losses. We provide results on composition of concave functions, notably regarding supergradient computations, that are key for developing generic dictionary learning algorithms applic...

Recently Burger et al. (2012) and Xie et al. (2012) proposed to use a denoising autoencoder (DAE) for denoising noisy images. They showed that a plain, deep DAE can denoise noisy images as well as the conventional methods such as BM3D and KSVD. Both of them approached image denoising by denoising small, image patches of a larger image and combining them to form a clean image. In this setting, i...

In this paper, we proposed a novel sparse coding algorithm by using the class labels to constrain the learning of codebook and sparse code. We not only use the class label to train the classifier, but also use it to construct class conditional codewords to make the sparse code as discriminative as possible. We first construct ideal sparse codes with regarding to the class conditional codewords,...

This paper proposes a novel filter bank composed of dominant Spectral Basis Vectors (SBVs) in a spectrogram. Spectral envelopes represented by the SBVs have shown to be excellent characteristic features for discriminating different acoustic events in noisy environment. Non-negative Matrix Factorization (NMF) and non-negative K-SVD (NKSVD) for part-based and holistic representations extract domi...

A novel Sparsely Encoded Local Descriptor (SELD) is proposed for face verification. Different from traditional hard or soft quantization methods, we exploit linear regression (LR) model with sparsity and non-negativity constraints to extract more discriminative features (i.e. sparse codes) from local image patches sampled pixel-wisely. Sum-pooling is then imposed to integrate all the sparse cod...

In the sparse representation model, the design of overcomplete dictionaries plays a key role for the effectiveness and applicability in different domains. Recent research has produced several dictionary learning approaches, being proven that dictionaries learnt by data examples significantly outperform structured ones, e.g. wavelet transforms. In this context, learning consists in adapting the ...

Character recognition, a specific problem in the area of pattern recognition is a sub-process in most of the Optical Character Recognition (OCR) systems. Singular Value Decomposition (SVD) is one of the promising and efficient dimensionality reduction methods, which is already applied and proved in the area of character recognition. Random Projection (RP) is a recently evolved dimension reducti...