基于协同表示的步态识别

将基于稀疏表示的分类算法应用于步态识别中,会遇到小样本及计算耗时的问题。针对这一问题,提出一种基于协同表示的步态识别方法。该方法首先通过背景重建、目标提取等处理获得人体侧影轮廓,根据步态轮廓的宽度变化统计步态周期,得到步态能量图GEI;其次,以GEI为基础对测试样本进行协同表示;最后,通过最小重构误差进行识别。实验结果表明,该方法具有较好的识别性能,并且识别时间明显降低。     

Query-expanded collaborative representation based classification with class-specific prototypes for object recognition

Linear representation based classifiers (LinearRCs) assume that a query image can be represented as a linear combination of dictionary atoms or prototypes with various priors (e.g., sparsity), which have achieved impressive results in face recognition. Recently, a few attempts have been made to deal with more general cases (e.g., multi-view or multi-pose objects, more generic objects, etc.) but with additional requirements. In this paper, we present a query-expanded collaborative representation based classifier with class-specific prototypes (QCRC_CP) from the general perspective. First, we expand a single query in a multi-resolution way to cover rich variations of object appearances, thereby generating a query set. We then condense the gallery images to a small amount of prototypical images by maximizing canonical correlation in a class-specific way, in which the implicit query-dependent data locality discards the outliers. Given the query set, we finally propose a multivariate LinearRC with collaborative prior to identify the query according to the rule of minimum normalized residual (MNR). Experiments on four object recognition datasets (FERET pose, Swedish leaf, Chars74K, and ETH-80) show that our method outperforms the state-of-the-art LinearRCs with performance increases at least 3.1%, 3.8%, 10.4% and 3.1% compared to other classifiers.     

基于稀疏编码的动态纹理识别

目的 线性动态系统有效地捕捉了动态纹理在时间和空间的转移信息。然而,线性动态系统属于非欧氏空间模型,无法直接应用传统的稀疏编码进行分类识别,为此提出一种基于稀疏编码线性动态系统的求解方法并应用于动态纹理识别。方法 基于约束凸优化公式,将稀疏编码和控制论中相似性变换结合,优化学习模型参数,解决应用稀疏编码进行分类识别的问题,实现有效的动态纹理识别。结果 在公开的动态纹理图像数据库UCLA上进行实验并与其他方法进行比较,实验结果表明,本文方法具有更好的性能,识别率可达97%,且对遮挡具有更好的鲁棒性。结论 本文方法对动态纹理及遮挡情况具有更好的识别率。     

Spacetime texture representation and recognition based on a spatiotemporal orientation analysis

This paper is concerned with the representation and recognition of the observed dynamics (i.e., excluding purely spatial appearance cues) of spacetime texture based on a spatiotemporal orientation analysis. The term "spacetime texture" is taken to refer to patterns in visual spacetime, (x,y,t), that primarily are characterized by the aggregate dynamic properties of elements or local measurements accumulated over a region of spatiotemporal support, rather than in terms of the dynamics of individual constituents. Examples include image sequences of natural processes that exhibit stochastic dynamics (e.g., fire, water, and windblown vegetation) as well as images of simpler dynamics when analyzed in terms of aggregate region properties (e.g., uniform motion of elements in imagery, such as pedestrians and vehicular traffic). Spacetime texture representation and recognition is important as it provides an early means of capturing the structure of an ensuing image stream in a meaningful fashion. Toward such ends, a novel approach to spacetime texture representation and an associated recognition method are described based on distributions (histograms) of spacetime orientation structure. Empirical evaluation on both standard and original image data sets shows the promise of the approach, including significant improvement over alternative state-of-the-art approaches in recognizing the same pattern from different viewpoints.     

基于SEF-GHEI及协同表示的步态识别研究

将协同表示方法应用于步态识别中可以解决稀疏表示方法计算耗时的问题,但提取步态特征采用的GEI算法没有考虑步态内部轮廓边界信息,导致识别率不高。针对此问题,本文提出使用融合Hog和GEI算法的方法提取步态特征,在此基础上使用协同表示的方法训练,再通过计算测试样本的最小重构误差进行分类。实验结果表明,该方法在单一视角下步态识别准确率平均提高了1.315%,以及跨视角下步态识别准确率平均提高了6.51%,说明本方法是可行的。     

Graph steered discriminative projections based on collaborative representation for Image recognition

Multimedia Tools and Applications - Dimensionality reduction techniques are commonly used for image recognition. We propose a graph steered dimensionality reduction method called Discriminative...     

基于协作表示残差融合的3维人脸识别

目的 针对2维人脸难以克服光照、表情、姿态等复杂问题,提出了一种基于协作表示残差融合的新算法.方法 协作表示分类算法是将所有类的训练图像一起协作构成字典,通过正则化最小二乘法代替1范数求解稀疏系数,减小了计算的复杂度,由此系数重构测试人脸,根据重构误差最小原则,对测试人脸正确分类.该方法首先在3维人脸深度图上提取Gabor特征和Geodesic特征,然后在协作表示算法的基础上融合两者的残差信息,作为最终差异性度量,最后根据融合残差最小原则,进行人脸识别.结果 在不同的训练样本、特征维数条件下,在CIS和Texas 2 个人脸数据库上,本文算法的识别率可分别达到94.545%和99.286%.与Gabor-CRC算法相比,本文算法的识别率平均高出了10%左右.结论 在实时成像系统采集的人脸库和Texas 3维人脸库上的实验结果表明,该方法对有无姿态、表情、遮挡等变化问题具有较好的鲁棒性和有效性.     

基于局部结构的多尺度协作表示人脸识别算法

人脸识别在实际应用中,往往存在无法获取足够多的训练样本的情况,而在小样本情况下,协作表示的识别性能会受到严重影响。多尺度块协作表示算法能有效集成不同尺度下的分类结果,但其分类框架中子块的计算是相互独立的,忽略了块之间的结构关系。而局部结构法将图像划分为多个局部区域,每个局部区域的重叠块分布在相同的线性子空间中,该子空间可以反应块之间的结构关系,能提高多尺度块协作表示在小样本下的鲁棒性。因此提出了基于局部结构的多尺度块协同表示算法（Local Structure based Multi-Patch Collaborative Representation,LS_MPCRC）,在Yale B和AR人脸库上的实验结果证明,该算法在训练样本数目较少时具有优秀的识别性能。     

基于低秩矩阵恢复与协同表征的人脸识别算法

针对人脸图像不完备的问题和人脸图像在不同视角、光照和噪声下所造成训练样本污损的问题,提出了一种快速的人脸识别算法--RPCA_CRC。首先,将人脸训练样本对应的矩阵D₀分解为类间低秩矩阵D和稀疏误差矩阵E;其次,以低秩矩阵D为基础,得到测试样本的协同表征;最后,通过重构误差进行分类。相对于基于稀疏表征的分类(SRC)方法,所提算法运行速度平均提高25倍;且在训练样本数不完备的情况下,识别率平均提升30%。实验证明该算法快速有效,识别率高。     

Multi-resolution dictionary collaborative representation for face recognition

Pattern Analysis and Applications - In this paper, a multi-resolution dictionary collaborative representation(MRDCR) method for face recognition is proposed. Unlike most of the traditional sparse...     

基于判别低秩矩阵恢复和协同表示的遮挡人脸识别

针对训练样本和测试样本均受到严重的噪声污染的人脸识别问题,传统的子空间学习方法和经典的基于稀疏表示的分类(SRC)方法的识别性能都将急剧下降。另外,基于稀疏表示的方法也存在算法复杂度较高的问题。为了在一定程度上缓解上述问题,提出一种基于判别低秩矩阵恢复和协同表示的遮挡人脸识别方法。首先,低秩矩阵恢复可以有效地从被污损的训练样本中恢复出干净的、具备低秩结构的训练样本,而结构非相关性约束的引入可以有效提高恢复数据的鉴别能力。然后,通过学习原始污损数据与恢复出的低秩数据之间的低秩投影矩阵,将受污损的测试样本投影到相应的低维子空间,以修正污损测试样本。最后,利用协同表示的分类方法(CRC)对修正后的测试样本进行分类,获取最终的识别结果。在Extended Yale B和AR数据库上的实验结果表明,本文方法对遮挡人脸识别具有更好的识别性能。     

基于近邻三值模式和协作表示的三维掌纹识别

针对二维掌纹图像存在易伪造、抗噪能力差的问题，提出一种基于近邻三值模式（NTP）和协作表示的三维掌纹识别方法。首先，利用形状指数把三维掌纹的表面几何信息映射成二维数据，以弥补常用均值或高斯曲率映射无法精确描述三维掌纹特征的缺陷；其次，对形状指数图作分块处理，利用近邻三值模式提取分块形状指数图的纹理特征；最后，利用协作表示的方法进行特征分类。在三维掌纹库上和经典算法进行的对比实验中，该方法的识别率为99.52%，识别时长为0.6738 s，优于其他算法；在识别率方面，与经典的局部二值模式（LBP）、局部三值模式（LLTP）、CompCode、均值曲率图（MCI）法相比分别提高了7.77%、6.02%、5.12%和3.97%；在识别时间方面，与Homotopy、对偶增广拉格朗日法（DALM）、SpaRSA方法相比分别降低了6.7 s、15.9 s和61 s。实验结果表明，所提算法具有良好的特征提取和分类能力，能够有效地提高识别精度并减少识别时间。     

Robust video summarization using collaborative representation of adjacent frames

Multimedia Tools and Applications - With the ever increasing volume of video content, efficient and effective video summarization (VS) techniques are urgently demanded to manage a large amount of...     

Supervised neighborhood regularized collaborative representation for face recognition

How to represent a test sample is very crucial for linear representation based classification. The famous sparse representation focuses on employing linear combination of small samples to represent the query sample. However, the local structure and label information of data are neglected. Recently, locality-constrained collaborative representation (LCCR) has been proposed and integrates a kind of locality-constrained term into the collaborative representation scheme. For each test sample, LCCR mainly considers its neighbors to deal with noise and LCCR is robust to various corruptions. However, the nearby samples may not belong to the same class. To deal with this situation, in this paper, we not only utilize the positive effect of neighbors, but also consider the side effect of neighbors. A novel supervised neighborhood regularized collaborative representation (SNRCR) is proposed, which employs the local structure of data and the label information of neighbors to improve the discriminative capability of the coding vector. The objective function of SNRCR obtains the global optimal solution. Many experiments are conducted over six face data sets and the results show that SNRCR outperforms other algorithms in most case, especially when the size of training data is relatively small. We also analyze the differences between SNRCR and LCCR.     

Learning locality-constrained collaborative representation for robust face recognition

The models of low-dimensional manifold and sparse representation are two well-known concise models that suggest that each data can be described by a few characteristics. Manifold learning is usually investigated for dimension reduction by preserving some expected local geometric structures from the original space into a low-dimensional one. The structures are generally determined by using pairwise distance, e.g., Euclidean distance. Alternatively, sparse representation denotes a data point as a linear combination of the points from the same subspace. In practical applications, however, the nearby points in terms of pairwise distance may not belong to the same subspace, and vice versa. Consequently, it is interesting and important to explore how to get a better representation by integrating these two models together. To this end, this paper proposes a novel coding algorithm, called Locality-Constrained Collaborative Representation (LCCR), which introduce a kind of local consistency into coding scheme to improve the discrimination of the representation. The locality term derives from a biologic observation that the similar inputs have similar codes. The objective function of LCCR has an analytical solution, and it does not involve local minima. The empirical studies based on several popular facial databases show that LCCR is promising in recognizing human faces with varying pose, expression and illumination, as well as various corruptions and occlusions.