Ziqi Zhu

Robust face recognition via occlusion dictionary learning

Sparse representation based classification (SRC) has recently been proposed for robust face recognition. To deal with occlusion, SRC introduces an identity matrix as an occlusion dictionary on the assumption that the occlusion has sparse representation in this dictionary. However, the results show that SRCs use of this occlusion dictionary is not nearly as robust to large occlusion as it is to random pixel corruption. In addition, the identity matrix renders the expanded dictionary large, which results in expensive computation. In this paper, we present a novel method, namely structured sparse representation based classification (SSRC), for face recognition with occlusion. A novel structured dictionary learning method is proposed to learn an occlusion dictionary from the data instead of an identity matrix. Specifically, a mutual incoherence of dictionaries regularization term is incorporated into the dictionary learning objective function which encourages the occlusion dictionary to be as independent as possible of the training sample dictionary. So that the occlusion can then be sparsely represented by the linear combination of the atoms from the learned occlusion dictionary and effectively separated from the occluded face image. The classification can thus be efficiently carried out on the recovered non-occluded face images and the size of the expanded dictionary is also much smaller than that used in SRC. The extensive experiments demonstrate that the proposed method achieves better results than the existing sparse representation based face recognition methods, especially in dealing with large region contiguous occlusion and severe illumination variation, while the computational cost is much lower.

An adaptive hybrid pattern for noise-robust texture analysis

Local binary patterns (LBP) achieve great success in texture analysis, however they are not robust to noise. The two reasons for such disadvantage of LBP schemes are (1) they encode the texture spatial structure based only on local information which is sensitive to noise and (2) they use exact values as the quantization thresholds, which make the extracted features sensitive to small changes in the input image. In this paper, we propose a noise-robust adaptive hybrid pattern (AHP) for noised texture analysis. In our scheme, two solutions from the perspective of texture description model and quantization algorithm have been developed to reduce the feature?s noise sensitiveness. First, a hybrid texture description model is proposed. In this model, the global texture spatial structure which is depicted by a global description model is encoded with the primitive microfeature for texture description. Second, we develop an adaptive quantization algorithm in which equal probability quantization is utilized to achieve the maximum partition entropy. Higher noise-tolerance can be obtained with the minimum lost information in the quantization process. The experimental results of texture classification on two texture databases with three different types of noise show that our approach leads significant improvement in noised texture analysis. Furthermore, our scheme achieves state-of-the-art performance in noisy face recognition. HighlightsA hybrid texture description model is proposed for noise-robust texture modeling.An adaptive quantization algorithm is designed for robust angular space quantization.Based on the new description model and quantization algorithm, we develop the AHP.Experimental results demonstrate the significant improvement achieved by our scheme.

Robust Nonnegative Patch Alignment for Dimensionality Reduction

Dimensionality reduction is an important method to analyze high-dimensional data and has many applications in pattern recognition and computer vision. In this paper, we propose a robust nonnegative patch alignment for dimensionality reduction, which includes a reconstruction error term and a whole alignment term. We use correntropy-induced metric to measure the reconstruction error, in which the weight is learned adaptively for each entry. For the whole alignment, we propose locality-preserving robust nonnegative patch alignment (LP-RNA) and sparsity-preserviing robust nonnegative patch alignment (SP-RNA), which are unsupervised and supervised, respectively. In the LP-RNA, we propose a locally sparse graph to encode the local geometric structure of the manifold embedded in high-dimensional space. In particular, we select large

Dynamic texture modeling and synthesis using multi-kernel Gaussian process dynamic model

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STFT-like time frequency representations of nonstationary signal with arbitrary sampling schemes

-nearest neighbors for each sample, then obtain the sparse representation with respect to these neighbors. The sparse representation is used to build a graph, which simultaneously enjoys locality, sparseness, and robustness. In the SP-RNA, we simultaneously use local geometric structure and discriminative information, in which the sparse reconstruction coefficient is used to characterize the local geometric structure and weighted distance is used to measure the separability of different classes. For the induced nonconvex objective function, we formulate it into a weighted nonnegative matrix factorization based on half-quadratic optimization. We propose a multiplicative update rule to solve this function and show that the objective function converges to a local optimum. Several experimental results on synthetic and real data sets demonstrate that the learned representation is more discriminative and robust than most existing dimensionality reduction methods.

Generalized Kernel Normalized Mixed-Norm Algorithm: Analysis and Simulations

Dynamic texture (DT) widely exists in various social video media. Therefore, DT modeling and synthesis plays an important role in social media analyzing and processing. In this paper, we propose a Bayesian-based nonlinear dynamic texture modeling method for dynamic texture synthesis. To capture the non-stationary distribution of DT, we utilize the Gaussian process latent variable model for dimensional reduction. Furthermore, we design a multi-kernel dynamic system for the latent dynamic behavior modeling. In our model, we do not make strong assumption on the nonlinear function. Instead, our model automatically constructs a suitable nonlinear kernel for dynamic modeling and therefore is capable of fitting various types of dynamics. We evaluate the effectiveness our methods on the DynTex database and compared with representative DT synthesis method. Experimental results show that our method can achieve synthesis results with higher visual quality. HighlightsA multi-kernel based Gaussian process dynamic model is proposed for dynamic texture modeling.We design a two-step optimization algorithm to learn the multi-kernel based Gaussian process dynamic model.We design a dynamic texture synthesis algorithm based on mean prediction for the proposed multi-kernel based Gaussian process dynamic model.

A Noise-Robust Adaptive Hybrid Pattern for Texture Classification

Spectrograms provide an effective way of time-frequency representation (TFR). Among these, short-time Fourier transform (STFT) based spectrograms are widely used for various applications. However, STFT spectrogram and its revised versions suffer from two main issues: (1) there is a trade-off between time resolution and frequency resolution and (2) almost all the existing TFR methods, including STFT spectrogram, are not designed to handle arbitrary nonuniformly sampled data. To address these two issues, short-time iterative adaptive approach (ST-IAA) was recently proposed as a data-dependent adaptive spectral estimation method that can provide much enhanced TFR performance. In this paper, inspired by the ST-IAA method, we present an alternative approach, namely short-time sparse learning via iterative minimization (ST-SLIM), which can provide sparser and slightly better TFR performance than its ST-IAA counterpart. Moreover, in order to extend the applicability of ST-IAA to signals in the missing data case, we also propose a short-time missing-data iterative adaptive approach (ST-MIAA) which can retrieve the missing data effectively and outperform ST-IAA and ST-SLIM in the missing data case. We will demonstrate via simulation results the superiority of our proposed algorithms in terms of resolution, sidelobe suppression and applicability to signals with arbitrary sampling patterns.

Robustness and stability of pure impulsive synchronization with parametric uncertainties and mismatch

This paper is a continuation and extension of our previous research where kernel normalized mixed-norm KNMN algorithm, a combination of the kernel trick with the mixed-norm strategy, was proposed to demonstrate superior performance for system identification under non-Gaussian environment. Meanwhile, we also introduced a naive adaptive mixing parameter AMP updating mechanism to make KNMN more robust under nonstationary scenarios. The main contributions of this paper are threefold: firstly, the

Dynamic Texture Synthesis via Image Reconstruction

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