基于集成模型的BOM近似度量方法

为满足多品种小批次、大规模定制模式下有效划分产品族的需求，全面分析BOM（Bill of Materials，物料清单）所包含的特征，概括已有结构近似方法并提出内容近似度量模型，在此基础上提出组合两者的集成模型.结构近似模型方面，以包含BOM层次结构和物料数量的相邻矩阵表示BOM，利用正交普氏分析法计算BOM与BOM之间的近似程度.内容近似模型方面，从BOM文本中提取有效特征，引入逆向词频法将文本特征转换成机器可识别向量形式，采用余弦近似公式完成向量近似的计算.集成模型提出基于基尼系数的权重分配方法集成结构和内容两种模型.最后，提供测试框架并通过实验评价集成模型较已有方法在模型性能及训练耗时上的优劣.     

一种基于深度度量学习的视频分类方法

针对视频分类中普遍面临的类内离散度和类间相似性较大而制约分类性能的问题,该文提出一种基于深度度量学习的视频分类方法。该方法设计了一种深度网络,网络包含特征学习、基于深度度量学习的相似性度量,以及分类3个部分。其中相似性度量的工作原理为:首先,计算特征间的欧式距离作为样本之间的语义距离;其次,设计一个间隔分配函数,根据语义距离动态分配语义间隔;最后,根据样本语义间隔计算误差并反向传播,使网络能够学习到样本间语义距离的差异,自动聚焦于难分样本,以充分学习难分样本的特征。该网络在训练过程中采用多任务学习的方法,同时学习相似性度量和分类任务,以达到整体最优。在UCF101和HMDB51上的实验结果表明,与已有方法相比,提出的方法能有效提高视频分类精度。     

一种基于图像分割技术和深度学习的高融合面部迷彩设计方法

针对传统面部迷彩设计主观性强、背景相似度差等问题,提出一种基于图像分割技术和深度学习的高融合面部迷彩设计方法。利用OpenCV视觉算法库和基于MediaPipe机器学习框架的FaceMesh深度学习模型,构建高融合面部迷彩计算机辅助设计系统。该系统能够准确检测识别面部轮廓和特征点,提取背景特征并构建符合背景特性的迷彩斑块库,实现迷彩斑块在面部自动调用填充,自动生成与背景高度融合且符合面部特性的面部迷彩设计方案。通过建立相似度指标评价体系,对采用上述方法设计的面部迷彩的伪装效果进行了实验验证。结果表明,该方法能够有效提高面部迷彩设计的科学性及伪装效果,为单兵在战场上快速精准地实施面部伪装提供了可靠且有效的方案。     

Piecewise Maximal Similarity for Ad-hoc Social Networks

Computing Profile Similarity is a fundamental requirement in the area of Social Networks to suggest similar social connections that have high chance of being accepted as actual connection. Representing and measuring similarity appropriately is a pursuit of many researchers. Cosine similarity is a widely used metric that is simple and effective. This paper provides analysis of cosine similarity for social profiles and proposes a novel method to compute Piecewise Maximal Similarity between profiles. The proposed metric is 6% more effective to measure similarity than cosine similarity based on computations on real data.     

Multi-manifold metric learning for face recognition based on image sets

In this paper, we propose a new multi-manifold metric learning (MMML) method for the task of face recognition based on image sets. Different from most existing metric learning algorithms that learn the distance metric for measuring single images, our method aims to learn distance metrics to measure the similarity between manifold pairs. In our method, each image set is modeled as a manifold and then multiple distance metrics among different manifolds are learned. With these distance metrics, the intra-class manifold variations are minimized and inter-class manifold variations are maximized simultaneously. For each person, we learn a distance metric by using such a criterion that all the learned distance metrics are person-specific and thus more discriminative. Our method is extensively evaluated on three widely studied face databases, i.e., Honda/UCSD database, CMU MoBo database and YouTube Celebrities database, and compared to the state-of-the-arts. Experimental results are presented to show the effectiveness of the proposed method.     

平移变化性相似性学习的行人重识别算法

行人重识别的精确度主要取决于相似性度量方法和特征学习模型。现有的度量方法存在平移不变性的特点,会增加网络参数训练的难度。现有的几种特征学习模型只强调样本之间的绝对距离而忽略了正样本对和负样本对之间的相对距离,造成网络学习到的特征判别性不强。针对现有度量方法的缺点该文提出一种平移变化的距离度量方法,能够简化网络的优化并能高效度量图像之间的相似性。针对特征学习模型的不足,提出一种增大间隔的逻辑回归模型,模型通过增大正负样本对之间的相对距离,使得网络得到的特征判别性更强。实验中,在Market1501和CUHK03数据库上对所提度量方式和特征学习模型的有效性进行验证,实验结果表明,所提度量方式性能更好,其平均精确率超出马氏距离度量6.59%,且所提特征学习模型也取得了很好的性能,算法的平均精确率较现有的先进算法有显著提高。     

基于多尺度细节增强的面部表情识别方法

人类面部表情是其心理情绪变化的最直观刻画,不同人的面部表情具有很大差异,现有表情识别方法均利用面部统计特征区分不同表情,其缺乏对于人脸细节信息的深度挖掘。根据心理学家对面部行为编码的定义可以看出,人脸的局部细节信息决定了其表情意义。因此该文提出一种基于多尺度细节增强的面部表情识别方法,针对面部表情受图像细节影响较大的特点,提出利用高斯金字塔提取图像细节信息,并对图像进行细节增强,从而强化人脸表情信息。针对面部表情的局部性特点,提出利用层次结构的局部梯度特征计算方法,描述面部特征点局部形状特征。最后,使用支持向量机(SVM)对面部表情进行分类。该文在CK+表情数据库中的实验结果表明,该方法不仅验证了图像细节对面部表情识别过程的重要作用,而且在小规模训练数据下也能够得到非常好的识别结果,表情平均识别率达到98.19%。     

基于几何结构属性的光学和SAR影像自动配准

提出了一种基于几何结构属性的光学和合成孔径雷达(Synthetic Aperture Radar,SAR)影像配准方法.该方法引入了具有光照不变性的相位一致性模型进行影像特征提取,采用该模型的强度和方向信息构建了一种几何结构特征描述符—相位一致性方向直方图(histogram of orientated phase congruency,HOPC),并根据该描述符间的欧式距离定义了影像匹配的相似性测度(称为HOPC_n).该测度能表示影像间的几何结构相似性.通过选择4组光学和SAR影像进行试验,结果表明,HOPC_n能有效率的抵抗影像间的非线性辐射差异,并且其匹配性能好于经典的相似性测度.另外,也设计了一种基于HOPC_n自动的配准方法,试验结果证明了该方法的有效率和鲁棒性.     

Pain detection using batch normalized discriminant restricted Boltzmann machine layers

A system for automatic pain detection whereby pain-related features are extracted from facial images using a four-layer Convolutional Deep Belief Network (CDBN) is proposed in this study. The CDBN is trained by greedy layer-wise procedure whereby each added layer is trained as a Convolutional Restricted Boltzmann Machine (CRBM) by contrastive divergence. Since conventional CRBM is trained in a purely unsupervised manner, there is no guarantee that learned features are appropriate for the supervised task at hand. A discriminative objective based on between-class and within-class distances is proposed to adapt CRBM to learn task-related features. When discriminative and generative objectives are appropriately combined, a competitive classification performance can be achieved. Moreover, we introduced batch normalization (BN) units in the structure of the CRBM model to smooth optimization landscape and speed up the learning process. BN units come right before sigmoid units. Extracted features are then used to train a linear SVM to classify each frame into pain or no-pain classes. Extensive experiments on UNBC-McMaster Shoulder Pain database demonstrate the effectiveness of the proposed method for automatic pain detection.     

Comparison of ICA approaches for facial expression recognition

Independent component analysis (ICA) and Gabor wavelets extract the most discriminating features for facial action unit classification by employing either a cosine similarity measure (CSM) classifier or support vector machines (SVMs). So far, only the ICA approach, which is based on the InfoMax principle, has been tested for facial expression recognition. In this paper, in addition to the InfoMax approach, another five ICA approaches extract features from two facial expression databases. In particular, the Extended InfoMax ICA, the undercomplete ICA, and the nonlinear kernel-ICA approaches are exploited for facial expression representation for the first time. When applied to images, ICA treats the images as being mixtures of independent sources and decomposes them into an independent basis and the corresponding mixture coefficients. Two architectures for representing the images can be employed yielding either independent and sparse basis images or independent and sparse distributions of image representation coefficients. After feature extraction, facial expression classification is performed with the help of either a CSM classifier or an SVM classifier. A detailed comparative study is made with respect to the accuracy offered by each classifier. The correlation between the accuracy and the mutual information of independent components or the kurtosis is evaluated. Statistically significant correlations between the aforementioned quantities are identified. Several issues are addressed in the paper: (i) whether features having super- and sub-Gaussian distribution facilitate facial expression classification; (ii) whether a nonlinear mixture of independent sources improves the classification accuracy; and (iii) whether an increased “amount” of sparseness yields more accurate facial expression recognition. In addition, performance enhancements by employing leave-one-set of expressions-out and subspace selection are studied. Statistically significant differences in accuracy between classifiers using several feature extraction methods are also indicated.     

基于稀疏特征挑选和概率线性判别分析的表情识别研究

提出一种基于稀疏特征挑选（Sparse selection）和概率线性判别分析（Probabilistic linear discriminant analysis）的表情识别方法SS-PLDA.该方法由两部分构成：第一部分是使用稀疏的方法挑选出人脸与表情相关的区域,构造表情的完备特征集;第二部分是针对构造的表情完备特征集里仍含有一些其他信息,运用概率线性判别分析实现表情特征与干扰信息的分离,学习出一个只含有表情信息的子空间,最后基于该表情子空间进行表情识别分析.通过在CK+和JAFFE这两个数据库上面的实验,证实了基于稀疏特征挑选的方法可以得到识别性能的改善,且先使用特征挑选再对所挑选结果应用概率线性判别分析可以达到更好的提升效果.     

基于双目特征联合的无参考立体图像质量评价

通过模拟人类视觉系统(HVS)的双目视觉行为,提 出一种基于双目特征联合的无参考立 体图像质量评价(NR-SIQA)方法。首先分析立体视觉感知中的双目联合行为,提出 可应用于立体图像质量预 测的双目联合模型;然后采用学习和统计分析的方法,分别提取局部和全局特征并联合作 为感知特征; 最后采用机器学习算法,建立特征和质量的关系模型,并结合基于特征的双目联合模型预测 立体图像质量。实验结果表明,本文方法在对称立体图像库上的Pearson线性相关系数(PLCC)和Spearman等级系数(SRCC)高于0.93,在非对称库上高于0.87,优 于现有评价方法。     

基于自适应鲁棒在线度量学习的面部表情识别

针对自然面部表情识别中的噪声标记问题,提出了一种自适应鲁棒在线度量学习方法.首先,学习新的度量空间以增加不同面部表情的判别性;然后,定义敏感度和特异性来表征每个注释器;最后,引入表示真实类标签的潜在变量,在期望最大化架构中迭代求解距离度量和注释器的可靠性.在MFP和AR人脸数据库上的实验结果表明,相比其他几种较新的方法,本方法在自然表情识别方面能获得更高的识别精度,高兴表情识别率可高达99.7％,并且在一定程度上降低了计算开销.     

Expression transfer for facial sketch animation

This paper presents a hierarchical animation method for transferring facial expressions extracted from a performance video to different facial sketches. Without any expression example obtained from target faces, our approach can transfer expressions by motion retargetting to facial sketches. However, in practical applications, the image noise in each frame will reduce the feature extraction accuracy from source faces. And the shape difference between source and target faces will influence the animation quality for representing expressions. To solve these difficulties, we propose a robust neighbor-expression transfer (NET) model, which aims at modeling the spatial relations among sparse facial features. By learning expression behaviors from neighbor face examples, the NET model can reconstruct facial expressions from noisy signals. Based on the NET model, we present a hierarchical method to animate facial sketches. The motion vectors on the source face are adjusted from coarse to fine on the target face. Accordingly, the animation results are generated to replicate source expressions. Experimental results demonstrate that the proposed method can effectively and robustly transfer expressions by noisy animation signals.     

基于人脸相似度加权距离的非特定人表情识别

该文提出了一种用于非特定人表情识别的方法。首先,对测试人的初始表情特征进行高阶奇异值分解,得到测试人与训练集中所有人相关的表情特征。然后,根据相似的人有相似的表情的假设,计算人脸相似度加权距离,作为测试人的表情特征与标准的表情特征之间的相似性测度。通过加权的过程,可以有效地去除由于个体差异而造成的表情特征的差异,提高非特定人表情识别的鲁棒性。该文提出的方法在JAFFE数据库上进行了测试。对非特定人的表情识别实验表明,该文方法比传统的方法在识别率上有了提高。     

基于三元采样图卷积网络的半监督遥感图像检索

该文提出了一种基于三元采样图卷积网络的度量学习方法,以实现遥感图像的半监督检索。所提方法由三元图卷积网络(TGCN)和基于图的三元组采样(GTS)两部分组成。TGCN由3个具有共享权重的并行卷积神经网络和图卷积网络组成,用以提取图像的初始特征以及学习图像的图嵌入。通过同时学习图像特征以及图嵌入,TGCN能够得到用于半监督图像检索的有效图结构。接着,通过提出的GTS算法对图结构内隐含的图像相似性信息进行评价,以选择合适的困难三元组(Hard Triplet),并利用困难三元组组成的样本集合对模型进行有效快速的模型训练。通过TGCN和GTS的组合,提出的度量学习方法在两个遥感数据集上进行了测试。实验结果表明,TGCN-GTS具有以下两方面的优越性：TGCN能够根据图像及图结构学习到有效的图嵌入特征及度量空间;GTS有效评估图结构内隐含的图像相似性信息选择合适的困难三元组,显著提升了半监督遥感图像检索效果。     

基于二维Fisher线性判别的人脸耳组合识别

针对人脸易受到年龄、表情等影响,提出了脸和耳相结合的组合识别方法。利用二维Fisher线性判别(2DFLD)方法分别进行了脸、耳图像层和特征层的组合识别。在北京科技大学人耳库和ORL人脸库上进行实验,结果表明,图像层组合和特征层组合的识别率分别为97.5%、95.0%,分别比人脸识别提高了12.5%和10.0%,比人耳识别提高了5.0%和2.5%;与同样应用于组合识别的主成分分析(PCA)、二维PCA(2DPCA)比较,也取得了较好识别效果。这说明,多生物特征组合识别是一种有效的识别方法。     

A similarity learning approach to content-based image retrieval: application to digital mammography

In this paper, we describe an approach to content-based retrieval of medical images from a database, and provide a preliminary demonstration of our approach as applied to retrieval of digital mammograms. Content-based image retrieval (CBIR) refers to the retrieval of images from a database using information derived from the images themselves, rather than solely from accompanying text indices. In the medical-imaging context, the ultimate aim of CBIR is to provide radiologists with a diagnostic aid in the form of a display of relevant past cases, along with proven pathology and other suitable information. CBIR may also be useful as a training tool for medical students and residents. The goal of information retrieval is to recall from a database information that is relevant to the user's query. The most challenging aspect of CBIR is the definition of relevance (similarity), which is used to guide the retrieval machine. In this paper, we pursue a new approach, in which similarity is learned from training examples provided by human observers. Specifically, we explore the use of neural networks and support vector machines to predict the user's notion of similarity. Within this framework we propose using a hierarchal learning approach, which consists of a cascade of a binary classifier and a regression module to optimize retrieval effectiveness and efficiency. We also explore how to incorporate online human interaction to achieve relevance feedback in this learning framework. Our experiments are based on a database consisting of 76 mammograms, all of which contain clustered microcalcifications (MCs). Our goal is to retrieve mammogram images containing similar MC clusters to that in a query. The performance of the retrieval system is evaluated using precision-recall curves computed using a cross-validation procedure. Our experimental results demonstrate that: 1) the learning framework can accurately predict the perceptual similarity reported by human observers, thereby serving as a basis for CBIR; 2) the learning-based framework can significantly outperform a simple distance-based similarity metric; 3) the use of the hierarchical two-stage network can improve retrieval performance; and 4) relevance feedback can be effectively incorporated into this learning framework to achieve improvement in retrieval precision based on online interaction with users; and 5) the retrieved images by the network can have predicting value for the disease condition of the query.