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  • 期刊名称:

    Pattern recognition letters

  • 中文名称: 模式识别字母
  • 刊频: 1.303
  • ISSN: 0167-8655
  • 出版社: -
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  • 机译 客座社论:图像/视频理解和分析
    • 作者:;
    • 刊名:Pattern recognition letters
    • 2020年第Feb.期
    摘要:
  • 机译 使用张量-SVD的加权张量核规范最小化以完成张量
    • 作者:;
    • 刊名:Pattern recognition letters
    • 2020年第Feb.期
    摘要: In this paper, we consider the tensor completion problem, which aims to estimate missing values from limited information. Our model is based on the recently proposed tensor-SVD, which uses the relationships among the color channels in an image or video recovery problem. To improve the availability of the model, we propose the weighted tensor nuclear norm whose weights are fixed in the algorithm, study its properties and prove the Karush-Kuhn-Tucker (KKT) conditions of the proposed algorithm. We conduct extensive experiments to verify the recovery capability of the proposed algorithm. The experimental results demonstrate improvements in computation time and recovery effect compared with related methods. (C) 2018 Elsevier B.V. All rights reserved.
  • 机译 模态相关的稀疏表示,用于RGB红外对象跟踪
    • 作者:;
    • 刊名:Pattern recognition letters
    • 2020年第Feb.期
    摘要: To intelligently analyze and understand video content, a key step is to accurately perceive the motion of the interested objects in videos. To this end, the task of object tracking, which aims to determine the position and status of the interested object in consecutive video frames, is very important, and has received great research interest in the last decade. Although numerous algorithms have been proposed for object tracking in RGB videos, most of them may fail to track the object when the information from the RGB video is not reliable (e.g. in dim environment or large illumination change). To address this issue, with the popularity of dual-camera systems for capturing RGB and infrared videos, this paper presents a feature representation and fusion model to combine the feature representation of the object in RGB and infrared modalities for object tracking. Specifically, this proposed model is able to (1) perform feature representation of objects in different modalities by employing the robustness of sparse representation, and (2) combine the representation by exploiting the modality correlation. Extensive experiments demonstrate the effectiveness of the proposed method. (C) 2018 Elsevier B.V. All rights reserved.
  • 机译 基于联合稀疏编码的深度图像超分辨率
    • 作者:;
    • 刊名:Pattern recognition letters
    • 2020年第Feb.期
    摘要: This paper proposes a new approach to single depth image super-resolution (SR), based upon a novel joint sparse coding model. A low-resolution color is used as a guide in the SR process. Firstly, we introduce synthetic characteristic image patch to learn a joint dictionary from the low-resolution depth map as well as its corresponding low-resolution intensity image. Then, we derive the joint nonlocal center sparse representation model based on sparse coding and theoretical analysis. In reconstruction process, we use Bayesian interpretation approach to estimation the sparse code coefficients for each unknown HR image patch. Meanwhile, we use an iterative algorithm to solve the JSC model. In addition, we exploit image patch redundancy within and across different scales, produce visually pleasing results without extensive training on external database. Experimental results demonstrate that the proposed method outperforms favorably many current state-of-the-art depth map super-resolution approaches on both visual effects and objective image quality and underpin the validity of our proposed model. Published by Elsevier B.V.
  • 机译 基于分割和传播的快速自动人像消光方法
    • 作者:;
    • 刊名:Pattern recognition letters
    • 2020年第Feb.期
    摘要: Image matting is a process of extracting objects from background in an image, which is an important work in digital image processing and video editing. Previous methods have poor performance and most methods need trimap or scribble to compute to get accurate image matting results. In this paper a quick automatic head image matting method for certificate photo production is put forward, which could rapidly extract satisfied head photo from images that are shoot by portable camera. First a new training data set about hair matting is created, and the image is segmented into different regions according to different gray level; then we detect and locate the face and eyes to adjust the correct head position; Finally, the accurate hair pixels are extracted from the edge region around head by multimodal Gaussian process regression. It's the advantage that the regions with clear foreground and background could be quickly extracted by segmented method, and the regions with similar foreground and background colors or complicated textures were labeled (the edge regions with hair around head), thus the hair could be extracted in smaller area. In our method the quick and accurate extracting of head photo needn't the trimap or scribbling or lots of training. Experimental results clearly demonstrate the superiority of our algorithm over previous methods. (C) 2019 Elsevier B.V. All rights reserved.
  • 机译 基于卷积神经网络的联合空间光谱高光谱图像分类
    • 作者:;
    • 刊名:Pattern recognition letters
    • 2020年第Feb.期
    摘要: Hyperspectral image (HSI) classification technology has been widely used in many earth observation tasks, such as detection, recognition, and surveillance. The traditional hyperspectral image classification methods mainly utilize hand-crafted features, such as edge and texture descriptors, which are not robust for different input data. By contrast, deep learning based methods exploit high-level features for hyperspectral image classification, but they usually degenerate the spatial-spectral structure, depend on a large number of training samples, and ignore a large amount of implicitly useful information. To address these problems, a new joint spatial-spectral hyperspectral image classification method based on different-scale two-stream convolutional network and spatial enhancement strategy is proposed in this paper. First, the pixel blocks at different scales around the center pixel are selected as the basic units to be processed. Then, a spatial enhancement strategy is designed to obtain various spatial location information under the limited training samples by the spatial rotation and row-column transformation. Finally, the spatial-spectral feature is learned by a different-scale two-stream convolutional network, and the classification result of the center pixel is obtained by a softmax layer. Experimental results on two datasets demonstrate that the proposed method outperforms other state-of-the-art methods qualitatively and quantitatively. (C) 2018 Elsevier B.V. All rights reserved.
  • 机译 通过协作网络中的知识领域嵌入发现软件专家
    • 作者:;
    • 刊名:Pattern recognition letters
    • 2020年第Feb.期
    摘要: Community Question Answering (CQA) websites can be claimed as the most major venues for knowledge sharing, and the most effective way of exchanging knowledge at present. Considering that massive amount of users are participating online and generating huge amount data, management of knowledge here systematically can be challenging. Expert recommendation is one of the major challenges, as it highlights users in CQA with potential expertise, which may help match unresolved questions with existing high quality answers while at the same time may help external services like human resource systems as another reference to evaluate their candidates. In this paper, we in this work we propose to exploring experts in CQA websites. We take advantage of recent distributed word representation technology to help summarize text chunks, and in a semantic view exploiting the relationships between natural language phrases to extract latent knowledge domains. By domains, the users' expertise is determined on their historical performance, and a rank can be compute to given recommendation accordingly. In particular, Stack Overflow is chosen as our dataset to test and evaluate our work, where inclusive experiment shows our competence. (C) 2018 Elsevier B.V. All rights reserved.
  • 机译 FC-RCCN:用于语义分割的全卷积残差连续CRF网络
    • 作者:;
    • 刊名:Pattern recognition letters
    • 2020年第Feb.期
    摘要: Enlarging the spatial resolution of features generated by fully convolutional networks (FCNs) can improve the performance of semantic segmentation. To achieve this goal, deeper network with deconvolutional structure can be applied. However, when the network architecture becomes more complex, the training efficiency may degrade. To address the joint optimization problem of improving spatial resolution through deeper networks and training deeper networks more effectively, we propose a Fully Convolutional Residual Continuous CRF Network (FC-RCCN) for semantic segmentation. FC-RCCN is composed of three subnetworks: a unary network, a pairwise network, and a superpixel based continuous conditional random filed (C-CRF) network. In order to generate full spatial resolution predictions with high-quality, a residual block based unary network with multi-scale features fusion is proposed. Even though the unary network is a deeper network, the whole framework can be trained effectively in an end-to-end way using the joint pixel-level and superpixel-level supervised learning strategy which is optimized by a pixel-level softmax cross entropy loss and a superpixel-level log-likelihood loss. Besides, C-CRF inference is fused with pixel-level prediction during the test procedure, which guarantees the method's robustness to the superpxiel errors. In the experiments, we evaluatee the power of the three subnetworks and the learning strategy comprehensively. Experiments on three benchmark datasets demonstrate that the proposed FC-RCCN outperforms previous segmentation methods and obtains the state-of-the-art performance. (C) 2018 Elsevier B.V. All rights reserved.
  • 机译 视觉显着性指导复杂图像检索
    • 作者:;
    • 刊名:Pattern recognition letters
    • 2020年第Feb.期
    摘要: Compared with the traditional text data, multimedia data are concise and contains rich meanings, so people are more willing to use the multimedia data to store information. How to effectively retrieve information is essential. This paper proposes a novel visual saliency guided complex image retrieval model. Initially, Itti visual saliency model is presented. In this model, the overall saliency map is generated by the integration of direction, intensity and color saliency map, respectively. Then, to help describe the image pattern more clearly, we present the multi-feature fusion paradigm of images. To address the complexity of the images, we propose a two-stage definition: (1) Cognitive load based complexity; (2) Cognitive level of complexity classification. The group sparse logistic regression model is integrated to finalize the image retrieval system. The performance of the proposed system is tested on different databases compared with the other state-of-the-art models which overcome the baselines in complex scenarios. (C) 2018 Elsevier B.V. All rights reserved.
  • 机译 用于草图识别的混合卷积神经网络
    • 作者:;
    • 刊名:Pattern recognition letters
    • 2020年第Feb.期
    摘要: With the popularity of touch-screen devices, it is becoming increasingly important to understand users' free-hand sketches in computer vision and human-computer interaction. Most of existing sketch recognition methods employ the similar strategies used in image recognition, relying on appearance information represented by hand-crafted features or deep features from convolutional neural networks. We believe that sketch recognition can benefit from learning both appearance and shape representation. In this paper, we propose a novel architecture, named Hybrid CNN, which is composed of A-Net and S-Net. They describe appearance information and shape information, respectively. Hybrid CNN is then comprehensively evaluated in the sketch classification and retrieval tasks on different datasets, including TU-Berlin, Sketchy and Flickr15k. Experimental results demonstrate that the Hybrid CNN achieves competitive accuracy compared with the state-of-the-art methods. (C) 2019 Elsevier B.V. All rights reserved.
  • 机译 非度量监督学习中核心集模型的稀疏性
    • 作者:;
    • 刊名:Pattern recognition letters
    • 2020年第Jan.期
    摘要: Supervised learning employing positive semi definite kernels has gained wide attraction and lead to a variety of successful machine learning approaches. The restriction to positive semi definite kernels and a hilbert space is common to simplify the mathematical derivations of the respective learning methods, but is also limiting because more recent research indicates that non-metric, and therefore non positive semi definite, data representations are often more effective. This challenge is addressed by multiple approaches and recently dedicated algorithms for so called indefinite learning have been proposed. Along this line, the Krein space Support Vector Machine (KSVM) and variants are very efficient classifiers for indefinite learning problems, but with a non-sparse decision function. This very dense decision function prevents practical applications due to a costly out of sample extension. We focus on this problem and provide two post processing techniques to sparsify models as obtained by a Krein space SVM approach. In particular we consider the indefinite Core Vector Machine and indefinite Core Vector Regression Machine which are both efficient for psd kernels, but suffer from the same dense decision function, if the Krein space approach is used. We evaluate the influence of different levels of sparsity and employ a Nystrom approach to address large scale problems. Experiments show that our algorithm is similar efficient as the non-sparse Krein space Support Vector Machine but with substantially lower costs, such that also problems of larger scale can be processed. (C) 2019 Elsevier B.V. All rights reserved.
  • 机译 GF-Net:通过功能门提高机器阅读理解
    • 作者:;
    • 刊名:Pattern recognition letters
    • 2020年第Jan.期
    摘要: Machine reading comprehension (MRC) is a field of question-answering in which computers understand given passages and answer related questions. Several previous models have tried to combine the use of linguistic and word embedding features to improve the performance of MRC; however, they could not obtain successful results because of feature interference problems caused by simple concatenation of the two. To resolve these problems, a machine reading comprehension model called gated feature network (GF-Net) is proposed in which linguistic features are selectively used according to their roles in the process of answer selection. In the GF-Net, the weights of the linguistic features are automatically controlled through gate mechanisms called feature gates. In the experiments with Stanford Question Answering Dataset SQuAD, the MRC models with feature gates showed a 0.67%p higher average of exact match (EM) and 0.64%p higher average of F1-score than models without feature gates. In addition, the GF-Net outperformed the previous MRC models to which feature gates were added. Based on these experimental results, it is concluded that the gate mechanism can contribute to an improvement in the performance of MRC models and the architecture of the GF-Net is suitable for the task of MRC. (C) 2019 Elsevier B.V. All rights reserved.
  • 机译 关于``学习与多类神经网络匹配的纠错图''的评论,模式识别字母,2018年
    • 作者:;
    • 刊名:Pattern recognition letters
    • 2020年第Jan.期
    摘要: We analyze the learning graph-matching algorithm presented in M. Martineau, R. Raveaux, D. Conte, G.Venturini, Learning error-correcting graph matching with a multiclass neural network, Pattern Recognition Letters (2018). Authors propose a new definition of the graph edit distance and also a learning algorithm that deduces some weights on this new graph edit distance. In this commentary, we first show that this new definition of the graph edit distance cannot be considered a distance and then, we discuss how this fact influences on the application of their learning methodology. (C) 2019 Elsevier B.V. All rights reserved.
  • 机译 改进了本地搜索图编辑距离
    • 作者:;
    • 刊名:Pattern recognition letters
    • 2020年第Jan.期
    摘要: The graph edit distance (GED) measures the dissimilarity between two graphs as the minimal cost of a sequence of elementary operations transforming one graph into another. This measure is fundamental in many areas such as structural pattern recognition or classification. However, exactly computing GED is NP-hard. Among different classes of heuristic algorithms that were proposed to compute approximate solutions, local search based algorithms provide the tightest upper bounds for GED. In this paper, we present K-REFINE and RANDPOST. K-REFINE generalizes and improves an existing local search algorithm and performs particularly well on small graphs. RANDPOST is a general warm start framework that stochastically generates promising initial solutions to be used by any local search based GED algorithm. It is particularly efficient on large graphs. An extensive empirical evaluation demonstrates that both K-REFINE and RANDPOST perform excellently in practice. (C) 2019 Elsevier B.V. All rights reserved.
  • 机译 肯德尔形状空间中基于3D离散地标模型度量的种族分类
    • 作者:;
    • 刊名:Pattern recognition letters
    • 2020年第Jan.期
    摘要: Knowledge of human ethnicity constitutes important biometric information. An automated ethnicity classification is a good first step in facial analysis. However, most ethnicity classification methods require a complex feature extraction and model training process. We propose a novel ethnicity classification method based on the analysis of facial landmarks in Kendall shape space. Facial features with different relative positions have a close relationship with ethnicity. Facial landmarks can represent positions of facial features. We build a Discrete Landmarks Model (DLM) based on facial landmarks and construct an ethnicity classification model based on the DLM analysis. The clear advantages of our method are that it is fully automated; requires no complex data preprocessing, feature extraction or a complex training process; results in a fast and accurate classification process. We estimate the effectiveness of our method experimentally, using public databases such as Texas3D, FRGC2.0, BU-3DFE and BU-4DFE. On average, our method can achieve a 95% ethnicity classification rate with each classification attempt in 2.0 s. (C) 2019 Elsevier B.V. All rights reserved.
  • 机译 组件树中的增量位四进制计数:理论,算法和优化
    • 作者:;
    • 刊名:Pattern recognition letters
    • 2020年第Jan.期
    摘要: Component tree is a full image representation which encodes all connected components from upper (resp. lower) level sets of a given image through the inclusion relation. Information from this representation can be used in many image processing and computational vision applications, e.g. connected filtering, image segmentation, feature extraction, among others. In general, each node of a component tree represents a connected component of a level set and stores attributes which describes features of this connected component. This paper presents a review of a previously published method to compute attributes such as area, perimeter, and number of Euler by incrementally counting patterns while traversing nodes of a component tree. This method foundation is further detailed in this paper by presenting a novel theoretical background and algorithm correctness intuition. We also present a novel approach for this algorithm showing improvements for run-time execution and precision analysis. (C) 2019 Elsevier B.V. All rights reserved.
  • 机译 分水岭层次结构的组合空间用于图像表征
    • 作者:;
    • 刊名:Pattern recognition letters
    • 2020年第Jan.期
    摘要: We propose a framework for image characterization using hierarchies of segmentations. For this purpose, we structure the space of hierarchies using the Gromov-Hausdorff distance. We propose different ways of combining hierarchies and study their properties thanks to the GH distance. We then expose how to leverage the combinatorial space of hierarchies to derive efficient image representations. This framework opens a path for a controlled exploration and use of the combinatorial space of hierarchies. (C) 2019 Elsevier B.V. All rights reserved.
  • 机译 简化合并金字塔定义的合并和简化操作
    • 作者:;
    • 刊名:Pattern recognition letters
    • 2020年第Jan.期
    摘要: Image pyramids are employed for years in digital image processing. They permit to store and use different scales/levels of details of an image. To represent all the topological information of the different levels, combinatorial pyramids have proved having many interests. But, when using an explicit representation, one drawback of this structure is the memory space required to store such a pyramid. In this paper, this drawback is solved by defining a compact version of combinatorial pyramids. This definition is based on the definition of a new operation, called "merge-and-simplify", which simultaneously merges regions and simplifies their boundaries. Our experiments show that the memory space of our solution is much smaller than the one of the original version. Moreover, the computation time of our solution is faster, because there are less levels in our pyramid than in the original one. (C) 2019 Elsevier B.V. All rights reserved.
  • 机译 用卷积神经网络学习艺术史原理
    • 作者:;
    • 刊名:Pattern recognition letters
    • 2020年第Jan.期
    摘要: Understanding the historical transformation of artistic styles implies the recognition of different stylistic properties. From a computer vision perspective, stylistic properties represent complex image features. In our work we explore the use of convolutional neural networks for learning features that are relevant for understanding properties of artistic styles. We focus on stylistic properties described by Heinrich Wolfflin in his book Principles of Art History (1915). Wolfflin identified five key visual principles, each defined by two contrasting concepts. We refer to each principle as one high-level image feature that measures how much each of the contrasting concepts is present in an image. We introduce convolutional neural network regression models trained to predict values of the five Wolfflin's features. We provide quantitative and qualitative evaluations of those predictions, as well as analyze how the predicted values relate to different styles and artists. The outcome of our analysis suggests that the models learn to discriminate meaningful features that correspond to the visual characteristics of concepts described by Wolfflin. This indicates that the presented approach can be used to enable new ways of exploring fine art collections based on image features relevant and well-known within art history. (C) 2019 Elsevier B.V. All rights reserved.
  • 机译 用于语义文本分割和定位的多尺度顺序网络
    • 作者:;
    • 刊名:Pattern recognition letters
    • 2020年第Jan.期
    摘要: We present a novel method for semantic text document analysis which in addition to localizing text it labels the text in user-defined semantic categories. More precisely, it consists of a fully-convolutional and sequential network that we apply to the particular case of slide analysis to detect title, bullets and standard text. Our contributions are twofold: (1) A multi-scale network consisting of a series of stages that sequentially refine the prediction of text and semantic labels (text, title, bullet); (2) A synthetic database of slide images with text and semantic annotation that is used to train the network with abundant data and wide variability in text appearance, slide layouts, and noise such as compression artifacts.We evaluate our method on a collection of real slide images collected from multiple conferences, and show that it is able to localize text with an accuracy of 95%, and to classify titles and bullets with accuracies of 94% and 85% respectively. In addition, we show that our method is competitive on scene and born-digital image datasets, such as ICDAR 2011, where it achieves an accuracy of 91.1%. (C) 2019 Elsevier B.V. All rights reserved.
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