Xiangwei Kong

Robust Principal Component Analysis Based on Maximum Correntropy Criterion

Principal component analysis (PCA) minimizes the mean square error (MSE) and is sensitive to outliers. In this paper, we present a new rotational-invariant PCA based on maximum correntropy criterion (MCC). A half-quadratic optimization algorithm is adopted to compute the correntropy objective. At each iteration, the complex optimization problem is reduced to a quadratic problem that can be efficiently solved by a standard optimization method. The proposed method exhibits the following benefits: 1) it is robust to outliers through the mechanism of MCC which can be more theoretically solid than a heuristic rule based on MSE; 2) it requires no assumption about the zero-mean of data for processing and can estimate data mean during optimization; and 3) its optimal solution consists of principal eigenvectors of a robust covariance matrix corresponding to the largest eigenvalues. In addition, kernel techniques are further introduced in the proposed method to deal with nonlinearly distributed data. Numerical results demonstrate that the proposed method can outperform robust rotational-invariant PCAs based on L1 norm when outliers occur.

Two-Stage Nonnegative Sparse Representation for Large-Scale Face Recognition

This paper proposes a novel nonnegative sparse representation approach, called two-stage sparse representation (TSR), for robust face recognition on a large-scale database. Based on the divide and conquer strategy, TSR decomposes the procedure of robust face recognition into outlier detection stage and recognition stage. In the first stage, we propose a general multisubspace framework to learn a robust metric in which noise and outliers in image pixels are detected. Potential loss functions, including L1 , L2,1, and correntropy are studied. In the second stage, based on the learned metric and collaborative representation, we propose an efficient nonnegative sparse representation algorithm to find an approximation solution of sparse representation. According to the L1 ball theory in sparse representation, the approximated solution is unique and can be optimized efficiently. Then a filtering strategy is developed to avoid the computation of the sparse representation on the whole large-scale dataset. Moreover, theoretical analysis also gives the necessary condition for nonnegative least squares technique to find a sparse solution. Extensive experiments on several public databases have demonstrated that the proposed TSR approach, in general, achieves better classification accuracy than the state-of-the-art sparse representation methods. More importantly, a significant reduction of computational costs is reached in comparison with sparse representation classifier; this enables the TSR to be more suitable for robust face recognition on a large-scale dataset.

A regularized correntropy framework for robust pattern recognition

This letter proposes a new multiple linear regression model using regularized correntropy for robust pattern recognition. First, we motivate the use of correntropy to improve the robustness of the classical mean square error (MSE) criterion that is sensitive to outliers. Then an l1 regularization scheme is imposed on the correntropy to learn robust and sparse representations. Based on the half-quadratic optimization technique, we propose a novel algorithm to solve the nonlinear optimization problem. Second, we develop a new correntropy-based classifier based on the learned regularization scheme for robust object recognition. Extensive experiments over several applications confirm that the correntropy-based l1 regularization can improve recognition accuracy and receiver operator characteristic curves under noise corruption and occlusion.

Nonnegative sparse coding for discriminative semi-supervised learning

An informative and discriminative graph plays an important role in the graph-based semi-supervised learning methods. This paper introduces a nonnegative sparse algorithm and its approximated algorithm based on the l0–l1 equivalence theory to compute the nonnegative sparse weights of a graph. Hence, the sparse probability graph (SPG) is termed for representing the proposed method. The nonnegative sparse weights in the graph naturally serve as clustering indicators, benefiting for semi-supervised learning. More important, our approximation algorithm speeds up the computation of the nonnegative sparse coding, which is still a bottle-neck for any previous attempts of sparse non-negative graph learning. And it is much more efficient than using l1-norm sparsity technique for learning large scale sparse graph. Finally, for discriminative semi-supervised learning, an adaptive label propagation algorithm is also proposed to iteratively predict the labels of data on the SPG. Promising experimental results show that the nonnegative sparse coding is efficient and effective for discriminative semi-supervised learning.

Large-scale image retrieval based on boosting iterative quantization hashing with query-adaptive reranking

Image hashing based Approximate Nearest Neighbor (ANN) searching has drawn more and more attention in large-scale image dataset applications. It is still challenging to learn hashing codes to achieve good search performance. In this paper, we propose an image retrieval method based on boosting iterative quantization hashing method with query-adaptive reranking. Firstly, in boosting iterative quantization hashing embedding, we adopt boosting-based method to generate inputs to learn hashing functions. Then we optimize the hashing functions with a loss function by considering the relationship between samples. Once the hashing codes are generated, Query-Adaptive Reranking (QAR) method is proposed to learn bit-level weights for each category and query-adaptive weights for each hashing bit. In this way, the discrete Hamming distance value can be continuous, and many irrelevant returned images can be sorted to the back. We conduct experiments on three public datasets, and comparison results with six state-of-the-art methods to illustrate the effectiveness of the proposed method.

Semantic consistency hashing for cross-modal retrieval

The task of cross-modal retrieval is to query similar objects in dataset of multi-modality, such as using text to query images and vice versa. However, most of existing works suffer from high computational complexity and storage cost in large-scale applications. Recently, hashing method mapping the high-dimensional data to compact binary codes has attracted a lot of concerns due to its efficiency and low storage cost over large-scale dataset. In this paper, we propose a Semantic Consistency Hashing (SCH) method for cross-modal retrieval. SCH learns a shared semantic space simultaneously taking both inter-modal and intra-modal semantic correlations into account. In order to preserve the inter-modal semantic consistency, an identical representation is learned using non-negative matrix factorization for the samples with different modalities. Meanwhile, neighbor preserving algorithm is adopted to preserve the semantic consistency in each modality. In addition, an effective optimal algorithm is proposed to reduce the time complexity from traditional O ( N 2 ) or higher to O(N). Extensive experiments on two public datasets demonstrate that the proposed approach significantly outperforms the existing schemes.

Disrupting MIMO Communications With Optimal Jamming Signal Design

This paper considers the problem of intelligent jamming attack on a MIMO wireless communication link with a transmitter, a receiver, and an adversarial jammer, each equipped with multiple antennas. We present an optimal jamming signal design, which can maximally disrupt the MIMO transmission when the transceiver adopts an anti-jamming mechanism. In particular, signal-to-jamming-plus-noise ratio (SJNR) at the receiver is used as the anti-jamming reliability metric of the legitimate MIMO transmission. The jamming signal design is developed under the most crucial scenario for the jammer where the legitimate transceiver adopt jointly designed maximum-SJNR transmit beamforming and receive filter to suppress/mitigate the disturbance from the jammer. Under this best anti-jamming scheme, we aim to optimize the jamming signal to minimize the receivers maximum-SJNR under a given jamming power budget. The optimal jamming signal designs are developed in different cases with accordance to the availability of channel state information (CSI) at the jammer. The analytical approximations of the jamming performance in terms of average maximum-SJNR are also provided. Extensive simulation studies confirm our analytical predictions and illustrate the efficiency of the designed optimal jamming signal on disrupting MIMO communications.

Printer forensics based on page document's geometric distortion

A printed document can provide intrinsic features of the printer so as to distinguish which printer it comes from. But how to extract the intrinsic features is critical in printer forensics. In this paper, the page documents geometric distortion is extracted as the intrinsic features, and a printer forensics method based on the distortion is proposed. Firstly projective transformation model is used to model the geometric distortion. After the feature point set of the model is extracted, the models parameters considered as the geometric distortion features can be estimated, and then the models error pattern can be obtained. During the process, the least squares method is used to estimate the models parameters, and SVM technique is used for classification. The effectiveness of the models parameters in the printer forensics is demonstrated by experimental results.

Exploiting Hierarchical Activations of Neural Network for Image Retrieval

The Convolutional Neural Networks (CNNs) have achieved breakthroughs on several image retrieval benchmarks. Most previous works re-formulate CNNs as global feature extractors used for linear scan. This paper proposes a Multi-layer Orderless Fusion (MOF) approach to integrate the activations of CNN in the Bag-of-Words (BoW) framework. Specifically, through only one forward pass in the network, we extract multi-layer CNN activations of local patches. Activations from each layer are aggregated in one BoW model, and several BoW models are combined with late fusion. Experimental results on two benchmark datasets demonstrate the effectiveness of the proposed method.

Generalized transfer component analysis for mismatched JPEG steganalysis

Most universal JPEG steganalysis approaches rely on the assumption that training and testing samples come from the same distribution. They fail when training set and testing set are mismatched. In this paper, we propose generalized transfer component analysis for mismatched JPEG steganalysis to derive new representations from original features for training and testing samples to correct the mismatches. We first apply domain alignment to transform source domain (training set) to an intermediate domain closer to target domain (testing set). Then a set of common transfer components are learnt across two domains by minimizing the distribution distance between them. In the space spanned by these transfer components, two domains manifest similar characteristics and preserve enough discrimination to different categories. Extensive experiments demonstrate our method performs well in mismatched JPEG steganalysis.