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  • 期刊名称:

    Pattern Analysis and Applications

  • 中文名称: 模式分析与应用
  • 刊频: 1.293
  • ISSN: 1433-7541
  • 出版社: -
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  • 机译 保局投影最小二乘支持向量机用于模式分类。
    摘要: During the last few years, multiple surface classification algorithms, such as twin support vector machine (TWSVM), least squares twin support vector machine (LSTSVM) and least squares projection twin support vector machine (LSPTSVM), have attracted much attention. However, these algorithms did not consider the local geometrical structure information of training samples. To alleviate this problem, in this paper, a locality preserving projection least squares twin support vector machine (LPPLSTSVM) is presented by introducing the basic idea of the locality preserving projection into LSPTSVM. This method not only inherits the ability of TWSVM, LSTSVM and LSPTSVM for pattern classification, but also fully considers the local geometrical structure between samples and shows the local underlying discriminatory information. Experimental results conducted on both synthetic and real-world datasets illustrate the effectiveness of the proposed LPPLSTSVM method.
  • 机译 基于融合特征约简和烟花算法优化的MSVM的混合控制图模式识别
    摘要: Unnatural control chart patterns (CCPs) can be associated with the quality problems of the production process. It is quite critical to detect and identify these patterns effectively based on process data. Various machine learning techniques to CCPs recognition have been studied on the process only suffer from basic CCPs of unnatural patterns. Practical production process data may be the combination of two or more basic patterns simultaneously in reality. This paper proposes a mixture CCPs recognition method based on fusion feature reduction (FFR) and fireworks algorithm-optimized multiclass support vector machine (MSVM). FFR algorithm consists of three main sub-networks: statistical and shape features, features fusion and kernel principal component analysis feature dimensionality reduction, which make the features more effective. In MSVM classifier algorithm, the kernel function parameters play a very significant role in mixture CCPs recognition accuracy. Therefore, fireworks algorithm is proposed to select the two-dimensional parameters of the classifier. The results of the proposed algorithm are benchmarked with popular genetic algorithm and particle swarm optimization methods. Simulation results demonstrate that the proposed method can gain the higher recognition accuracy and significantly reduce the running time.
  • 机译 使用语义上下文在静止图像中进行多个概念检测
    摘要: Multimedia documents indexing systems performances have been improved significantly in recent years, especially after the involvement of deep learning approaches. However, this progress remains insufficient with the evolution of users' needs that become complex in terms of semantics and the number of words that compose their queries. So, it is important to think about indexing images by a group of concepts simultaneously (multi-concepts) and not just single ones. This would allow systems to better respond to queries composed of several terms. This task is much more difficult than indexing images by single concepts. Multi-concepts detection in images has been little dealt in the state of the art compared to the detection of visual single concepts. On the other hand, the use of context has proved its effectiveness in the field of multimedia semantic indexing. In this work, we propose two approaches that consider the semantic context for multi-concepts detection in still images. We tested and evaluated our proposal on the international standard corpus Pascal VOC for the detection of concepts pairs and triplets of concepts. Our contributions have shown that context is useful and improves multi-concepts detection in images. The combination of the use of semantic context and deep learning-based features yielded much better results than those of the state of the art. This difference in performance is estimated by a relative gain on mean average precision reaching + 70% for concepts pairs and + 34% for the case of triplets of concepts.
  • 机译 基于频谱的通用二维任意形状滤波器组的设计:在隐形眼镜检测中的应用
    摘要: A filter bank (FB) is an integral part of any image processing system. The designing of a FB generally involves modifying an existing FB or focusing on a particular property of the filter bank. Such FBs limit their use to a particular image. Through our work, we have devised a unique and novel approach for designing a two-dimensional arbitrary shape filter bank (2-D ASFB). This FB is inherently 2-D and eliminates the need for transforming a one-dimensional FB into 2-D. Its arbitrary nature expands its application to any image as compared to regular-shaped FBs currently in use. The novelty of the design lies in the fact that the designed FB can match the frequency spectrum of any image by reducing the error function between the frequency spectrum and the desired filter response of the FB. The error function has been minimized using the eigenfilter approach. After designing the low-pass analysis filter, perfect reconstruction constraint has been used to get a low-pass synthesis filter. In this paper, we have demonstrated the use of the 2-D ASFB specifically for contact lens detection (CLD). The proposed CLD system focuses on feature extraction using the 2-D ASFB. The support vector machine classifier is the same as in the existing systems. The results show improved correct classification rate as compared to the existing systems for IIITD and ND2013 contact lens database. This 2-D ASFB overcomes limitations posed by the existing filter banks with respect to separability, directionality, orthogonality, and shape. This FB can be effectively applied to any feature extraction application such as pattern recognition, biometrics, medical image processing.
  • 机译 通过提取颜色的色度特征,使用模糊c-均值自动计算用于彩色图像分割的簇数
    摘要: In this paper we introduce a method for color image segmentation by computing automatically the number of clusters the data, pixels, are divided into using fuzzy c-means. In several works the number of clusters is defined by the user. In other ones the number of clusters is computed by obtaining the number of dominant colors, which is determined with unsupervised neural networks (NN) trained with the image's colors; the number of dominant colors is defined by the number of the most activated neurons. The drawbacks with this approach are as follows: (1) The NN must be trained every time a new image is given and (2) despite employing different color spaces, the intensity data of colors are used, so the undesired effects of non-uniform illumination may affect computing the number of dominant colors. Our proposal consists in processing the images with an unsupervised NN trained previously with chromaticity samples of different colors; the number of the neurons with the highest activation occurrences defines the number of clusters the image is segmented. By training the NN with chromatic data of colors it can be employed to process any image without training it again, and our approach is, to some extent, robust to non-uniform illumination. We perform experiments with the images of the Berkeley segmentation database, using competitive NN and self-organizing maps; we compute and compare the quantitative evaluation of the segmented images obtained with related works using the probabilistic random index and variation of information metrics.
  • 机译 基于Mumford-Shah模型的蒙版因子和邻域因子的图像分割方法
    摘要: A novel image segmentation model is proposed to improve the stability of existing segmentation methods. In the proposed model, we introduce two factors into the Mumford-Shah model, including mask factor and neighborhood factor. Firstly, the mask factor can express the image more accurately. Therefore, the new segmentation model can more realistically reflect the structure of the image. Moreover, neighborhood factor is used to constrain the evolution of the initial contour. Then the segmentation model is converted into an equivalent form by a level set function. At last, the model can be solved in a simple way based on partial differential equations and extreme values. The experimental results show the proposed method could generate accurate segmentation results, and the segmentation results are not sensitive to initial contour and external disturbances, such as noise and blurring.
  • 机译 走向依赖实例的标签耐噪分类:一种概率方法
    摘要: Learning from labelled data is becoming more and more challenging due to inherent imperfection of training labels. Existing label noise-tolerant learning machines were primarily designed to tackle class-conditional noise which occurs at random, independently from input instances. However, relatively less attention was given to a more general type of label noise which is influenced by input features. In this paper, we try to address the problem of learning a classifier in the presence of instance-dependent label noise by developing a novel label noise model which is expected to capture the variation of label noise rate within a class. This is accomplished by adopting a probability density function of a mixture of Gaussians to approximate the label flipping probabilities. Experimental results demonstrate the effectiveness of the proposed method over existing approaches.
  • 机译 从增量数据库中挖掘稀有关联规则
    摘要: Rare association rule mining is an imperative field of data mining that attempts to identify rare correlations among the items in a database. Although numerous attempts pertaining to rare association rule mining can be found in the literature, there are still certain issues that need utmost attention. The most prominent one among them is the rare association rule mining from incremental databases. The existing rare association rule mining techniques are capable of operating only on static databases, assuming that the entire database to be operated on is available during the outset of the mining process. Inclusion of new records, however, may lead to the generation of some new interesting rules from the current set of data, invalidating the previously extracted significant rare association rules. Executing the entire mining process from scratch for the newly arrived set of data could be a tedious affair. With a view to resolve the issue of incremental rare association rule mining, this study presents a single-pass tree-based approach for extracting rare association rules when new data are inserted into the original database. The proposed approach is capable of generating the complete set of frequent and rare patterns without rescanning the updated database and reconstructing the entire tree structure when new transactions are added to the existent database. Experimental evaluation has been carried out on several benchmark real and synthetic datasets to analyze the efficiency of the proposed approach. Furthermore, to assess its applicability in real-world applications, experimental analysis has been performed on a real geological dataset where earthquake records are incrementally being added on an annual basis. Comparative performance analysis demonstrates the preeminence of proposed approach over existing frequent and rare association rule mining techniques.
  • 机译 启发式迭代方案的网络,用于消除脉冲噪声
    摘要: This paper presents a supervised data-driven algorithm for impulse noise removal via iterative scheme-inspired network (IIN). IIN is defined over a data flow graph, which is derived from the iterative procedures in Alternating Direction Method of Multipliers (ADMM) algorithm for optimizing the L1-guided variational model. In the training phase, the L1-minimization is reformulated into an augmented Lagrangian scheme through adding a new auxiliary variable. In the testing phase, it has computational overhead similar to ADMM but uses optimized parameters learned from the training data for restoration task. Experimental results demonstrate that the newly proposed method can obtain very significantly superior performance than current state-of-the-art variational and dictionary learning-based approaches for salt-and-pepper noise removal.
  • 机译 了解视频字幕的时间结构
    摘要: Recent research in convolutional and recurrent neural networks has fueled incredible advances in video understanding. We propose a video captioning framework that achieves the performance and quality necessary to be deployed in distributed surveillance systems. Our method combines an efficient hierarchical architecture with novel attention mechanisms at both the local and global levels. By shifting focus to different spatiotemporal locations, attention mechanisms correlate sequential outputs with activation maps, offering a clever way to adaptively combine multiple frames and locations of video. As soft attention mixing weights are solved via back-propagation, the number of weights or input frames needs to be known in advance. To remove this restriction, our video understanding framework combines continuous attention mechanisms over a family of Gaussian distributions. Our efficient multistream hierarchical model combines a recurrent architecture with a soft hierarchy layer using both equally spaced and dynamically localized boundary cuts. As opposed to costly volumetric attention approaches, we use video attributes to steer temporal attention. Our fully learnable end-to-end approach helps predict salient temporal regions of action/objects in the video. We demonstrate state-of-the-art captioning results on the popular MSVD, MSR-VTT and M-VAD video datasets and compare several variants of the algorithm suitable for real-time applications. By adjusting the frame rate, we show a single computer can generate effective video captions for 100 simultaneous cameras. We additionally perform studies to show how bit rate compression modifies captioning results.
  • 机译 聚类大小分布对聚类的影响:k均值和模糊c均值聚类的比较研究
    摘要: Data distribution has a significant impact on clustering results. This study focuses on the effect of cluster size distribution on clustering, namely the uniform effect of k-means and fuzzy c-means (FCM) clustering. We first provide some related works of k-means and FCM clustering. Then, the structure decomposition analysis of the objective functions of k-means and FCM is presented. Afterward, extensive experiments on both synthetic two-dimensional and three-dimensional data sets and real-world data sets from the UCI machine learning repository are conducted. The results demonstrate that FCM has stronger uniform effect than k-means clustering. Also, it reveals that the fuzzifier value m = 2 in FCM, which has been widely adopted in many applications, is not a good choice, particularly for data sets with great variation in cluster sizes. Therefore, for data sets with significant uneven distributions in cluster sizes, a smaller fuzzifier value is preferred for FCM clustering, and k-means clustering is a better choice compared with FCM clustering.
  • 机译 监督机器学习中进化神经网络中基于过滤器的特征选择
    摘要: This paper presents a workbench to get simple neural classification models based on product evolutionary networks via a prior data preparation at attribute level by means of filter-based feature selection. Therefore, the computation to build the classifier is shorter, compared to a full model without data pre-processing, which is of utmost importance since the evolutionary neural models are stochastic and different classifiers with different seeds are required to get reliable results. Feature selection is one of the most common techniques for pre-processing the data within any kind of learning task. Six filters have been tested to assess the proposal. Fourteen (binary and multi-class) difficult classification data sets from the University of California repository at Irvine have been established as the test bed. An empirical study between the evolutionary neural network models obtained with and without feature selection has been included. The results have been contrasted with nonparametric statistical tests and show that the current proposal improves the test accuracy of the previous models significantly. Moreover, the current proposal is much more efficient than the previous methodology; the time reduction percentage is above 40%, on average. Our approach has also been compared with several classifiers both with and without feature selection in order to illustrate the performance of the different filters considered. Lastly, a statistical analysis for each feature selector has been performed providing a pairwise comparison between machine learning algorithms.
  • 机译 用于视觉目标跟踪的多任务非负矩阵分解
    摘要: This paper proposes an online object tracking algorithm in which the object tracking is achieved by using multi-task sparse learning and non-negative matrix factorization under the particle filtering framework. The object appearance is first modeled by subspace learning to reflect the target variations across frames. Combination of non-negative components is learned from examples observed in previous frames. In order to robust tracking an object, group sparsity constraints are included to the non-negativity one. Furthermore, the alternating direction method of multipliers algorithm is employed to compute the model efficiently. Qualitative and quantitative experiments on a variety of challenging sequences show favorable performance of the proposed algorithm against state-of-the-art methods.
  • 机译 基于进化Haar滤波器的人脸检测
    摘要: Face detection is considered to be one of the principal techniques of biometrics. Several methods for face detection have been proposed and described in the literature, but the Viola and Jones method is one of the most prominent. This method is based on the principle of Haar filters. In this study, we propose a new type of Haar filter called a dispersed Haar filter. This new structure provides more flexibility for very complex geometry, such as the human face. To create the structure of the filter, we used three optimizations methods: differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO). To test our approaches rigorously, we performed two types of tests. The first test is facial detection on fixed images from three different databases (Caltech 10K, FDDB, and CMU-MIT), which presents a significant challenge. The second test is more efficient and involves the recognition of human faces from a video database. For our experiment, we used a YouTube celebrity dataset. This system consists of two stages: Face detection using three detectors: Haar-DE, Haar-PSO, and Haar-GA. Face recognition using three machine-learning algorithms: multilayer perceptron (MLP), support vector machine (SVM), and convolutional neural network (CNN) with multi-scale images. The proposed Haar-DE algorithm demonstrates good detection performance on several databases compared with the state-of-the-art methods.
  • 机译 基于相似性传播聚类的自动灰度图像分割
    摘要: Image segmentation is an important research subject in the field of image analysis and pattern recognition. Based on the affinity propagation (AP) clustering algorithm, an automatic segmentation method is proposed for grayscale images. The AP algorithm is used for image segmentation to avoid the choice of initial clustering centers and enhance the stability of the segmentation results, and a new index is proposed to analyze the validity of segmentation results and determine the optimal number of segments. Moreover, gray values instead of pixels in gray space are clustered as the dataset to decrease the time complexity of the similarity matrix and validity analysis. Theoretical analysis and experimental results demonstrate the effectiveness of the proposed index and method.
  • 机译 用于多模态多类分类的相关最大化机
    摘要: Support vector machine (SVM) learns the maximum margin to separate training examples that belong to two classes and has been widely used in many pattern recognition tasks due to its high effectiveness. However, conventional SVM suffers from the following deficiencies: (1) SVM cannot take full advantage of multiple modalities in a dataset if they are available, and (2) SVM trains the marginal hyper-plane by solving the quadratic programming problem, and thus costs too much computational overheads. In this paper, we propose a correlation maximization machine (CMM) model to overcome the aforementioned deficiencies by integrating two modalities in a dataset to boost the classification performance and utilizing randomized nonlinear features to output labels from multiple classes. In particular, CMM reveals the nonlinear relationships among both modalities by generating randomized nonlinear features for each modality. CMM learns to project these features into a common subspace with a constraint that their coefficients are highly correlated, and narrows the gap between the coefficients of both modalities and the class indicator of each training example to deal with multiclass classification problem. At the classification stage, CMM indicates the classes of a test example by using the summation of its coefficients of both modalities. Since the objective function of CMM is non-convex, it is quite difficult to obtain the global minimum. In this paper, we developed a block coordinate descent-based algorithm to optimize CMM and theoretically proved its convergence to a local minimum. Experimental results of face recognition on three popular face image datasets and experimental results of image retrieval on CIFAR-10, NUS-Wide, and Wikipedia datasets demonstrate that CMM outperforms the representative methods.
  • 机译 基于熵的多视角矩阵完成与边信息的聚类
    摘要: Multi-view clustering aims to group multi-view samples into different clusters based on the similarity. Since side information can describe the relation between samples, for example, must-links and cannot-links, thus multi-view clustering with the consideration about side information along with samples can get more feasible clustering results. As a recent developed multi-view clustering approach, multi-view matrix completion (MVMC) constructs similarity matrix for each view and casts clustering into a matrix completion problem. Different from traditional multi-view clustering approaches, MVMC enforces the consistency of clustering results on different views as constraints for alternative optimization and the global optimal solution can be obtained. Although related experiments show that MVMC exhibits impressive performance, it still neglects the possibility of a sample belonging to a cluster. In this paper, we consider the possibility on the base of entropy and develop an entropy-based multi-view matrix completion for clustering with side information (EMVMC). Experiments on multi-view datasets Course, Citeseer, Cora, WebKB, NewsGroup, and Reuters validate the effectiveness of EMVMC.
  • 机译 一种新颖的高性能整体描述符用于人脸检索
    摘要: Texture extraction-based classification has become the facto methodology applied in face recognition. Haralick feature extraction from gray-level co-occurrence matrix (GLCM) is one of the basic holistic studies that has inspired many face recognition algorithms. This paper presents a theoretically simple, yet efficient, holistic approach that utilizes the spatial relationships of the same pixel patterns occurring at different positions in an image rather than their occurrence statistics as applied in GLCM-based counterparts. The matrix holding the statistical values for the total displacement of the pixel patterns is called the gray-level total displacement matrix (GLTDM). Three approaches are proposed for feature extraction. In the first approach, classical Haralick features extraction is conducted. The second approach (D_GLTDM) utilizes the GLTDM directly as the feature vector rather than extra feature extraction process. In the last approach, principle component analysis (PCA) is used as the feature extraction method. Comprehensive simulations are conducted on images retrieved from the popular face databases, namely face94, ORL, JAFFE and Yale. The performance of the proposed method is compared with that of GLCM, local binary pattern and PCA used in the leading studies. The simulation results and their comparative analysis show that D_GLTDM exhibits promising results and outperforms the other leading methods in terms of classification accuracy.
  • 机译 RTS-ELM:采用波纹变换进行显着性图像分割的方法
    摘要: In spite of great advancements in the field of computer vision in recent times, efficient identification of salient regions in an image/scene and applying the results to image segmentation are a fertile area to be explored by researchers. This paper deals with a novel approach for image segmentation called RTS-ELM which uses cues from salient region identification. Initially, salient regions of an image are identified using ripplet transform. Based on the saliency map, a trimap is generated for an image which highlights the dominant regions of an image. Using histogram analysis, the dominant pixels of foreground and background are grouped together to produce the positive and negative groups of training data. The salient regions are then segmented using the trained ELM classifier. After a rigmarole process of comparing with eleven extant approaches using three benchmark datasets, RTS-ELM is found to be an efficient method for reifying effective segmentation in different types of images with only a few errors.
  • 机译 在视频监控中使用步态功能进行实时,强大的多视图性别分类
    摘要: It is common to view people in real applications walking in arbitrary directions, holding items, or wearing heavy coats. These factors are challenges in gait-based application methods, because they significantly change a person's appearance. This paper proposes a novel method for classifying human gender in real time using gait information. The use of an average gait image, rather than a gait energy image, allows this method to be computationally efficient and robust against view changes. A viewpoint model is created for automatically determining the viewing angle during the testing phase. A distance signal model is constructed to remove any areas with an attachment (carried items, worn coats) from a silhouette to reduce the interference in the resulting classification. Finally, the human gender is classified using multiple-view-dependent classifiers trained using a support vector machine. Experiment results confirm that the proposed method achieves a high accuracy of 98.8% on the CASIA Dataset B and outperforms the recent state-of-the-art methods.
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