Image denoising using sparse representation classification and non-subsampled shearlet transform

In this paper, an image denoising method is proposed which uses sparse un-mixing by variable splitting and augmented Lagrangian (SUnSAL) classifier in the non-subsampled shearlet transform (NSST) domain. To this aim, the noisy image is decomposed into various scales and directional components using the NSST and then the feature vector for a pixel is constituted by the spatial regularity in the NSST domain. Subsequently, the NSST detail coefficients are labeled as edge-related coefficients or noise-related ones by using the SUnSAL classifier. The noisy coefficients of the NSST subbands are then denoised by the shrink method, which uses the adaptive Bayesian threshold for denoising. Finally, the inverse NSST transform is applied to the denoised coefficients. Our experiments demonstrate that the proposed approach improves the image quality in terms of both subjective and objective inspections, compared with some other state-of-the-art denoising techniques.     

Image classification using correlation tensor analysis

Images, as high-dimensional data, usually embody large variabilities. To classify images for versatile applications, an effective algorithm is necessarily designed by systematically considering the data structure, similarity metric, discriminant subspace, and classifier. In this paper, we provide evidence that, besides the Fisher criterion, graph embedding, and tensorization used in many existing methods, the correlation-based similarity metric embodied in supervised multilinear discriminant subspace learning can additionally improve the classification performance. In particular, a novel discriminant subspace learning algorithm, called correlation tensor analysis (CTA), is designed to incorporate both graph-embedded correlational mapping and discriminant analysis in a Fisher type of learning manner. The correlation metric can estimate intrinsic angles and distances for the locally isometric embedding, which can deal with the case when Euclidean metric is incapable of capturing the intrinsic similarities between data points. CTA learns multiple interrelated subspaces to obtain a low-dimensional data representation reflecting both class label information and intrinsic geometric structure of the data distribution. Extensive comparisons with most popular subspace learning methods on face recognition evaluation demonstrate the effectiveness and superiority of CTA. Parameter analysis also reveals its robustness.     

Image block classification using stochastic image segmentation

The authors propose a new image block classification method. The proposed algorithm incorporates image context into the classification via pixel-based segmentation. To obtain a segmented image they adopt the stochastic model-based unsupervised image segmentation algorithm. Since the block classifier considers the grey level distribution in the block, it can differentiate edges from textures. Also, since the segmentation is executed independently at each small block, a parallel processor can be applied to obtain a real-time block classification     

Image representation using block compressive sensing for compression applications

The emerging compressive sensing (CS) theory has pointed us a promising way of developing novel efficient data compression techniques, although it is proposed with original intention to achieve dimension-reduced sampling for saving data sampling cost. However, the non-adaptive projection representation for the natural images by conventional CS (CCS) framework may lead to an inefficient compression performance when comparing to the classical image compression standards such as JPEG and JPEG 2000. In this paper, two simple methods are investigated for the block CS (BCS) with discrete cosine transform (DCT) based image representation for compression applications. One is called coefficient random permutation (CRP), and the other is termed adaptive sampling (AS). The CRP method can be effective in balancing the sparsity of sampled vectors in DCT domain of image, and then in improving the CS sampling efficiency. The AS is achieved by designing an adaptive measurement matrix used in CS based on the energy distribution characteristics of image in DCT domain, which has a good effect in enhancing the CS performance. Experimental results demonstrate that our proposed methods are efficacious in reducing the dimension of the BCS-based image representation and/or improving the recovered image quality. The proposed BCS based image representation scheme could be an efficient alternative for applications of encrypted image compression and/or robust image compression.     

Image subband coding using context-based classification andadaptive quantization

Adaptive compression methods have been a key component of many proposed subband (or wavelet) image coding techniques. This paper deals with a particular type of adaptive subband image coding where we focus on the image coder's ability to adjust itself "on the fly" to the spatially varying statistical nature of image contents. This backward adaptation is distinguished from more frequently used forward adaptation in that forward adaptation selects the best operating parameters from a predesigned set and thus uses considerable amount of side information in order for the encoder and the decoder to operate with the same parameters. Specifically, we present backward adaptive quantization using a new context-based classification technique which classifies each subband coefficient based on the surrounding quantized coefficients. We couple this classification with online parametric adaptation of the quantizer applied to each class. A simple uniform threshold quantizer is employed as the baseline quantizer for which adaptation is achieved. Our subband image coder based on the proposed adaptive classification quantization idea exhibits excellent rate-distortion performance, in particular at very low rates. For popular test images, it is comparable or superior to most of the state-of-the-art coders in the literature.     

Image tamper detection and classification using support vector machines

The use of robust watermarks for attack characterisation is an area of considerable potential which has been largely overlooked to date. The authors extend their earlier work on accurate attack characterisation using a double watermarking technique to include a larger library of attacks. It is shown that the complexity of the double watermarking technique can be reduced with only a very small performance penalty. A further reduction in the algorithm complexity can be achieved by removing the thresholding process from the watermark estimation procedure. Analysis of the nature and location of the characterisation errors for the above methods is also presented.     

Fusion of hyperspectral data using segmented PCT for color representation and classification

Fusion of hyperspectral data is proposed by means of partitioning the hyperspectral bands into subgroups, prior to principal components transformation (PCT). The first principal component of each subgroup is employed for image visualization. The proposed approach is general, with the number of bands in each subgroup being application dependent. Nevertheless, the paper focuses on partitions with three subgroups suitable for RGB representation. One of them employs matched-filtering based on the spectral characteristics of various materials and is very promising for classification purposes. The information content of the hyperspectral bands as well as the quality of the obtained RGB images are quantitatively assessed using measures such as the correlation coefficient, the entropy, and the maximum energy-minimum correlation index. The classification performance of the proposed partitioning approaches is tested using the K-means algorithm.     

Image classification using spectral and spatial information basedon MRF models

A new criterion for classifying multispectral remote sensing images or textured images by using spectral and spatial information is proposed. The images are modeled with a hierarchical Markov Random Field (MRF) model that consists of the observed intensity process and the hidden class label process. The class labels are estimated according to the maximum a posteriori (MAP) criterion, but some reasonable approximations are used to reduce the computational load. A stepwise classification algorithm is derived and is confirmed by simulation and experimental results.     

Semi-supervised low-rank representation for image classification

Low-rank representation (LRR) is a useful tool for seeking the lowest rank representation among all the coefficient matrices that represent the images as linear combinations of the basis in the given dictionary. However, it is an unsupervised method and has poor applicability and performance in real scenarios because of the lack of image information. In this paper, based on LRR, we propose a novel semi-supervised approach, called label constrained sparse low-rank representation (LCSLRR), which incorporates the label information as an additional hard constraint. Specifically, this paper develops an optimization process in which the improvement of the discriminating power of the low-rank decomposition is presented explicitly by adding the label information constraint. We construct LCSLRR-graph to represent data structures for semi-supervised learning and provide the weights of edges in the graph by seeking a low-rank and sparse matrix. We conduct extensive experiments on publicly available databases to verify the effectiveness of our novel algorithm in comparison to the state-of-the-art approaches through a set of evaluations.     

Image deblocking via sparse representation

Image compression based on block-based Discrete Cosine Transform (BDCT) inevitably produces annoying blocking artifacts because each block is transformed and quantized independently. This paper proposes a new deblocking method for BDCT compressed images based on sparse representation. To remove blocking artifacts, we obtain a general dictionary from a set of training images using the K-singular value decomposition (K-SVD) algorithm, which can effectively describe the content of an image. Then, an error threshold for orthogonal matching pursuit (OMP) is automatically estimated to use the dictionary for image deblocking by the compression factor of compressed image. Consequently, blocking artifacts are significantly reduced by the obtained dictionary and the estimated error threshold. Experimental results indicate that the proposed method is very effective in dealing with the image deblocking problem from compressed images.     

Cluster-space representation for hyperspectral data classification

This paper presents a generalization of the hybrid supervised-unsupervised approach to image classification, and an automatic procedure for implementing it with hyperspectral data. Cluster-space representation is introduced in which clustered training data is displayed in a one-dimensional (1-D) cluster-space showing its probability distribution. This representation leads to automatic association of spectral clusters with information classes and the development of a cluster-space classification (CSC). Pixel labeling is undertaken by a combined decision based on its membership of belonging to defined clusters and the clusters' membership of belonging to information classes. The method provides a means of class data separability inspection, visually and quantitatively, regardless of the number of spectral bands used. The class modeling requires only that first degree statistics be estimated; therefore, the number of training samples required can be many fewer than when using Gaussian maximum likelihood (GML) classification. Experiments are presented based on computer generated data and AVIRIS data. The advantages of the method are demonstrated showing improved capacity for data classification     

多稀疏表示分类器决策融合修正距离的图像检索

针对当前许多图 像检索方法的检索精度不理想的问题,本文为增强图像特征的表达能力,通过统计图像的颜 色矩、多尺度分块 局部二值模式、灰度共生矩阵、尺度不变特征变换以及空间位置信息,提取5类能从不同角 度表征图像本 质特性的特征,并根据图像库中各训练图像的类别信息,以此5类特征构造5个稀疏表示分类 器,同时引 入决策融合思想,根据每个子分类器的分类性能,通过一个自适应迭代运算过程确定各子分 类器的融合权 值,以刻画不同类别特征的图像表达能力,并据此构造距离修正因子对不同特征所描述的图 像间距离进行 修正,从而得到综合各类特征表达能力的图像间的修正距离,实现图像的相似性评价,获得 检索结果。实 验结果表明,基于Corel-1000图像库,本文提出的方法平均查准率 为82.1%,比现有的方法平均提升10个百分点 ,而且鲁棒性更强。     

Visual classification with multitask joint sparse representation

We address the problem of visual classification with multiple features and/or multiple instances. Motivated by the recent success of multitask joint covariate selection, we formulate this problem as a multitask joint sparse representation model to combine the strength of multiple features and/or instances for recognition. A joint sparsity-inducing norm is utilized to enforce class-level joint sparsity patterns among the multiple representation vectors. The proposed model can be efficiently optimized by a proximal gradient method. Furthermore, we extend our method to the setup where features are described in kernel matrices. We then investigate into two applications of our method to visual classification: 1) fusing multiple kernel features for object categorization and 2) robust face recognition in video with an ensemble of query images. Extensive experiments on challenging real-world data sets demonstrate that the proposed method is competitive to the state-of-the-art methods in respective applications.     

基于低秩特征脸与协同表示的人脸识别算法

在人脸识别中,人脸图像往往受到表情、光照、遮挡、姿态变化的影响,对此本文提出一种基于低秩特征脸与协同表示的人脸识别算法。该算法先用低秩矩阵恢复算法分解出训练样本图像的误差图像,再分别对训练样本与误差图像提取特征构造特征字典,计算测试样本图像特征字典下的协同表示系数,最后通过重构误差进行分类。通过AR和ORL人脸库进行实验,结果表明,本文提出的人脸识别算法的识别率、识别速率得到有效提高。     

混合样本协同表示算法的人脸识别研究

在人脸识别中,人脸图像受到表情、光照、遮挡、姿态变化、特别是训练样本数量的影响,而现实中经常只获得少量的训练样本,由于原始样本生成虚拟样本可以增加训练样本的数量,分析提出原始样本与轴对称样本融合的协同表示算法。首先生成镜像样本与轴对称样本,再在协同表示分类器下分类,最后加权值融合,分析不同权值下的人脸识别率。实验结果显示原始样本、镜像样本与轴对称样本融合能提高识别率,而原始样本与轴对称样本融合的识别率更加优越,较原始样本,识别率提高2%~9%,比原始样本与镜像样本融合高1%~5%。结果表明本文提出方法能有效提高人脸识别率。     

Linear collaborative discriminant regression classification for face recognition

This paper proposes a novel face recognition method that improves Huang’s linear discriminant regression classification (LDRC) algorithm. The original work finds a discriminant subspace by maximizing the between-class reconstruction error and minimizing the within-class reconstruction error simultaneously, where the reconstruction error is obtained using Linear Regression Classification (LRC). However, the maximization of the overall between-class reconstruction error is easily dominated by some large class-specific between-class reconstruction errors, which makes the following LRC erroneous. This paper adopts a better between-class reconstruction error measurement which is obtained using the collaborative representation instead of class-specific representation and can be regarded as the lower bound of all the class-specific between-class reconstruction errors. Therefore, the maximization of the collaborative between-class reconstruction error maximizes each class-specific between-class reconstruction and emphasizes the small class-specific between-class reconstruction errors, which is beneficial for the following LRC. Extensive experiments are conducted and the effectiveness of the proposed method is verified.     

Locality-sensitive kernel sparse representation classification for face recognition

In this paper a new classification method called locality-sensitive kernel sparse representation classification (LS-KSRC) is proposed for face recognition. LS-KSRC integrates both sparsity and data locality in the kernel feature space rather than in the original feature space. LS-KSRC can learn more discriminating sparse representation coefficients for face recognition. The closed form solution of the l₁-norm minimization problem for LS-KSRC is also presented. LS-KSRC is compared with kernel sparse representation classification (KSRC), sparse representation classification (SRC), locality-constrained linear coding (LLC), support vector machines (SVM), the nearest neighbor (NN), and the nearest subspace (NS). Experimental results on three benchmarking face databases, i.e., the ORL database, the Extended Yale B database, and the CMU PIE database, demonstrate the promising performance of the proposed method for face recognition, outperforming the other used methods.     

Image segmentation towards new image representation methods

Very low bit-rate video coding has recently become one of the most important areas of image communication and a large variety of applications have already been identified. Since conventional approaches are reaching a saturation point, in terms of coding efficiency, a new generation of video coding techniques, aiming at a deeper “understanding” of the image, is being studied. In this context, image analysis, particularly the identification of objects or regions in images (segmentation), is a very important step. This paper describes a segmentation algorithm based on split and merge. Images are first simplified using mathematical morphology operators, which eliminate perceptually less relevant details. The simplified image is then split according to a quad tree structure and the resulting regions are finally merged in three steps: merge, elimination of small regions and control of the number of regions.