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  • Low-Rank Adaptive Graph Embedding for Unsupervised Feature Extraction
    Pattern Recogn. (IF 7.196) Pub Date : 2020-11-20
    Jianglin Lu; Hailing Wang; Jie Zhou; Yudong Chen; Zhihui Lai; Qinghua Hu

    Most of manifold learning based feature extraction methods are two-step methods, which first construct a weighted neighborhood graph and then use the pre-constructed graph to perform subspace learning. As a result, these methods fail to use the underlying correlation structure of data to learn an adaptive graph to preciously characterize the similarity relationship between samples. To address this

    更新日期:2020-11-21
  • Crossover-Net: Leveraging Vertical-horizontal Crossover Relation for Robust Medical Image Segmentation
    Pattern Recogn. (IF 7.196) Pub Date : 2020-11-13
    Qian Yu; Yang Gao; Yefeng Zheng; Jianbing Zhu; Yakang Dai; Yinghuan Shi

    Accurate boundary segmentation in medical images is significant yet challenging due to large variation of shape, size and appearance within intra- and inter- samples. In this paper, we present a novel deep model termed as Crossover-Net for robust segmentation in medical images. The proposed model is inspired by an interesting observation – the features learned from horizontal and vertical directions

    更新日期:2020-11-13
  • Contrast-weighted Dictionary Learning Based Saliency Detection for VHR Optical Remote Sensing Images
    Pattern Recogn. (IF 7.196) Pub Date : 2020-11-13
    Zhou Huang; Huai-Xin Chen; Tao Zhou; Yun-Zhi Yang; Chang-Yin Wang; Bi-Yuan Liu
    更新日期:2020-11-13
  • Exploring a Unified Low Rank Representation for Multi-focus Image Fusion
    Pattern Recogn. (IF 7.196) Pub Date : 2020-11-13
    Qiang Zhang; Fan Wang; Yongjiang Luo; Jungong Han

    Recent years have witnessed a trend that uses image representation models, including sparse representation (SR), low-rank representation (LRR) and their variants for multi-focus image fusion. Despite the thrilling preliminary results, existing methods conduct the fusion patch by patch, leading to insufficient consideration of the spatial consistency among the image patches within a local region or

    更新日期:2020-11-13
  • Deep Video Code for Efficient Face Video Retrieval
    Pattern Recogn. (IF 7.196) Pub Date : 2020-11-12
    Shishi Qiao; Ruiping Wang; Shiguang Shan; Xilin Chen

    In this paper, we address one specific video retrieval problem in terms of human face. Given one query in forms of either a frame or a sequence from a person, we search the database and return the most relevant face videos, i.e., ones have the same class label with the query. Such problem is very challenging due to the large intra-class variations and the high request on the efficiency of video representations

    更新日期:2020-11-12
  • Hyperspectral image classification via discriminative convolutional neural network with an improved triplet loss
    Pattern Recogn. (IF 7.196) Pub Date : 2020-11-11
    Ke-Kun Huang; Chuan-Xian Ren; Hui Liu; Zhao-Rong Lai; Yu-Feng Yu; Dao-Qing Dai

    Hyper-Spectral Image (HSI) classification is an important task because of its wide range of applications. With the remarkable success from the Convolutional Neural Network (CNN), the performance of HSI classification has been significantly improved. However, two main challenges remained. One is that the samples of HSI have dramatic intra-class diversity and inter-class similarity, and the conventional

    更新日期:2020-11-12
  • Deep neural network oriented evolutionary parametric eye modelling
    Pattern Recogn. (IF 7.196) Pub Date : 2020-11-09
    Yang ZHENG; Hong FU; Ruimin LI; Tai-Chiu Hsung; Zongxi SONG; Desheng WEN

    Comprehensive and accurate eye modelling is crucial to a variety of applications, including human-computer interaction, assistive technologies, and medical diagnosis. However, most studies focus on the localization of one or two components of eyes, such as pupil or iris, lacking a comprehensive eye model. We propose to model an eye image by a set of parametric curves. The set of curves are plotted

    更新日期:2020-11-09
  • GraphAIR: Graph Representation Learning with Neighborhood Aggregation and Interaction
    Pattern Recogn. (IF 7.196) Pub Date : 2020-11-08
    Fenyu Hu; Yanqiao Zhu; Shu Wu; Weiran Huang; Liang Wang; Tieniu Tan

    Graph representation learning is of paramount importance for a variety of graph analytical tasks, ranging from node classification to community detection. Recently, graph convolutional networks (GCNs) have been successfully applied for graph representation learning. These GCNs generate node representation by aggregating features from the neighborhoods, which follows the “neighborhood aggregation” scheme

    更新日期:2020-11-09
  • Low-Rank Tensor Ring Learning for Multi-linear Regression
    Pattern Recogn. (IF 7.196) Pub Date : 2020-11-07
    Jiani Liu; Ce Zhu; Zhen Long; Huyan Huang; Yipeng Liu

    The emergence of large-scale data demands new regression models with multi-dimensional coefficient arrays, known as tensor regression models. The recently proposed tensor ring decomposition has interesting properties of enhanced representation and compression capability, cyclic permutation invariance and balanced tensor ring rank, which may lead to efficient computation and fewer parameters in regression

    更新日期:2020-11-09
  • Infrared Small Target Detection via Adaptive M-Estimator Ring Top-Hat Transformation
    Pattern Recogn. (IF 7.196) Pub Date : 2020-11-07
    Lizhen Deng; Jieke Zhang; Guoxia Xu; Hu Zhu

    Top-Hat transformation is an essential technology in the field of infrared small target detection. Many modified Top-Hat transformation methods have been proposed based on the different structure of structural elements. However, these methods are still hard to handle the dim targets and complex background. It can be summarized as two reasons, one is that the structural elements cannot suppress the

    更新日期:2020-11-09
  • A three-step classification framework to handle complex data distribution for radar UAV detection
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-22
    Jianfeng Ren; Xudong Jiang

    Unmanned aerial vehicles (UAVs) have been used in a wide range of applications and become an increasingly important radar target. To better model radar data and to tackle the curse of dimensionality, a three-step classification framework is proposed for UAV detection. First we propose to utilize the greedy subspace clustering to handle potential outliers and the complex sample distribution of radar

    更新日期:2020-11-06
  • Enhancing the alignment between target words and corresponding frames for video captioning
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-14
    Yunbin Tu; Chang Zhou; Junjun Guo; Shengxiang Gao; Zhengtao Yu

    Video captioning aims at translating from a sequence of video frames into a sequence of words with the encoder-decoder framework. Hence, it is critical to align these two different sequences. Most existing methods exploit soft-attention (temporal attention) mechanism to align target words with corresponding frames, where the relevance of them merely depends on the previously generated words (i.e.,

    更新日期:2020-11-06
  • Alignment-free cancelable fingerprint templates with dual protection
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-30
    Muhammad Shahzad; Song Wang; Guang Deng; Wencheng Yang

    Cancelable biometrics is an important biometric template protection technique. However, many existing cancelable fingerprint templates suffer post-transformation performance deterioration and the attacks via record multiplicity (ARM). In this paper, we design alignment-free cancelable fingerprint templates with dual protection, which is composed of the window-shift-XOR model and the partial discrete

    更新日期:2020-11-06
  • Fast high-precision ellipse detection method
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-31
    Zepeng Wang; Derong Chen; Jiulu Gong; Changyuan Wang

    Obtaining an optimal tradeoff between accuracy and efficiency in ellipse detection is a significant challenge. In this paper, we propose a fast, high-precision ellipse detection method that utilizes arc selection and grouping strategies to significantly reduce the computation amount. A fast corner detection algorithm is also proposed. In the proposed method, to generate ellipse candidates comprehensively

    更新日期:2020-11-06
  • Deep feature augmentation for occluded image classification
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-31
    Feng Cen; Xiaoyu Zhao; Wuzhuang Li; Guanghui Wang

    Due to the difficulty in acquiring massive task-specific occluded images, the classification of occluded images with deep convolutional neural networks (CNNs) remains highly challenging. To alleviate the dependency on large-scale occluded image datasets, we propose a novel approach to improve the classification accuracy of occluded images by fine-tuning the pre-trained models with a set of augmented

    更新日期:2020-11-06
  • Biometric key generation based on generated intervals and two-layer error correcting technique
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-30
    Peiyi Wang; Lin You; Gengran Hu; Liqin Hu; Zhihua Jian; Chaoping Xing

    As for a biometric key, key management and biometric data security are both important. Existing bio-key generation methods are usually based on the biometric templates or features directly, it may expose user’s biometric data and will further make the biometric data permanently unusable for his secure identification recognitions. In this paper, a fingerprint bio-key generation approach using the feature

    更新日期:2020-11-06
  • Cluster-wise unsupervised hashing for cross-modal similarity search
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-29
    Lu Wang; Jie Yang; Masoumeh Zareapoor; Zhonglong Zheng

    Cross-modal hashing similarity retrieval plays dual roles across various applications including search engines and autopilot systems. More generally, these methods also known to reduce the computation and memory storage in a training scheme. The key limitation of current methods are that: (i) they relax the discrete constrains to solve the optimization problem which may defeat the model purpose, (ii)

    更新日期:2020-11-06
  • Kernel Two-Dimensional Ridge Regression for Subspace Clustering
    Pattern Recogn. (IF 7.196) Pub Date : 2020-11-05
    Chong Peng; Qian Zhang; Zhao Kang; Chenglizhao Chen; Qiang Cheng

    Subspace clustering methods have been extensively studied in recent years. For 2-dimensional (2D) data, existing subspace clustering methods usually convert 2D examples to vectors, which severely damages inherent structural information and relationships of the original data. In this paper, we propose a novel subspace clustering method, named KTRR, for 2D data. The KTRR provides us with a way to learn

    更新日期:2020-11-06
  • Cross-modal Discriminant Adversarial Network
    Pattern Recogn. (IF 7.196) Pub Date : 2020-11-05
    Peng Hu; Xi Peng; Hongyuan Zhu; Jie Lin; Liangli Zhen; Wei Wang; Dezhong Peng

    Cross-modal retrieval aims at retrieving relevant points across different modalities, such as retrieving images via texts. One key challenge of cross-modal retrieval is narrowing the heterogeneous gap across diverse modalities. To overcome this challenge, we propose a novel method termed as Cross-modal discriminant Adversarial Network (CAN). Taking bi-modal data as a showcase, CAN consists of two parallel

    更新日期:2020-11-06
  • Joint Adaptive Manifold and Embedding Learning for Unsupervised Feature Selection
    Pattern Recogn. (IF 7.196) Pub Date : 2020-11-04
    Jian-Sheng Wu; Meng-Xiao Song; Weidong Min; Jian-Huang Lai; Wei-Shi Zheng

    As data always lie on a lower-dimensional space, feature selection has become an important step in computer vision, machine learning and data mining. Due to the lack of class information, the performance of unsupervised feature selection depends on how to characterize and preserve the manifold structure among data. In this paper, we propose a novel unsupervised feature selection framework, named as

    更新日期:2020-11-04
  • An evidential clustering algorithm by finding belief-peaks and disjoint neighborhoods
    Pattern Recogn. (IF 7.196) Pub Date : 2020-11-04
    Chaoyu Gong; Zhi-gang Su; Pei-hong Wang; Qian Wang

    In this paper, we introduce a new evidential clustering algorithm based on finding the belief-peaks and disjoint neighborhoods, called BPDNEC. The basic idea of BPDNEC is that each cluster center has the highest possibility of becoming a cluster center among its neighborhood and neighborhoods of those cluster centers are disjoint in vector space. Such possibility is measured by the belief notion in

    更新日期:2020-11-04
  • A hierarchical weighted low-rank representation for image clustering and classification
    Pattern Recogn. (IF 7.196) Pub Date : 2020-11-04
    Zhiqiang Fu; Yao Zhao; Dongxia Chang; Yiming Wang

    Low-rank representation(LRR), which is a powerful method to find the low-dimensional subspace structure embedded in high-dimensional data spaces, has been used in both unsupervised learning and semi-supervised classification. LRR aims at finding the lowest rank representation that can express each data sample as linear combination of other samples. However, this method doesn’t consider the geometrical

    更新日期:2020-11-04
  • EF-Net: A Novel Enhancement and Fusion Network for RGB-D Saliency Detection
    Pattern Recogn. (IF 7.196) Pub Date : 2020-11-04
    Qian Chen; Keren Fu; Ze Liu; Geng Chen; Hongwei Du; Bensheng Qiu; Ling Shao

    Salient object detection (SOD) has gained tremendous attention in the field of computer vision. Multi-modal SOD based on the complementary information from RGB images and depth maps has shown remarkable success, making RGB-D saliency detection an active research topic. In this paper, we propose a novel multi-modal enhancement and fusion network (EF-Net) for effective RGB-D saliency detection. Specifically

    更新日期:2020-11-04
  • Face illumination recovery for the deep learning feature under severe illumination variations
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-24
    Chang-Hui Hu; Jian Yu; Fei Wu; Yang Zhang; Xiao-Yuan Jing; Xiao-Bo Lu; Pan Liu

    The deep learning feature is the best for face recognition nowadays, but its performance exhibits unsatisfactorily under severe illumination variations. The main reason is that the deep learning feature was trained by the internet face images with variations of large pose/expression and slight/moderate illumination, which cannot well tackle severe illumination variations. Inspired by the fact that

    更新日期:2020-11-02
  • SI(FS)2: Fast simultaneous instance and feature selection for datasets with many features
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-24
    Nicolás García-Pedrajas; Juan A. Romero del Castillo; Gonzalo Cerruela-García

    Data reduction is becoming increasingly relevant due to the enormous amounts of data that are constantly being produced in many fields of research. Instance selection is one of the most widely used methods for this task. At the same time, most recent pattern recognition problems involve highly complex datasets with a large number of possible explanatory variables. For many reasons, this abundance of

    更新日期:2020-11-02
  • Adaptive feature fusion for visual object tracking
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-21
    Shaochuan Zhao; Tianyang Xu; Xiao-Jun Wu; Xue-Feng Zhu

    Recent advanced trackers, consisting of discriminative classification component and dedicated bounding box estimation, have achieved improved performance in the visual tracking community. The most essential factor for the development is the utilization of different Convolutional Neural Networks (CNNs), which significantly improves the model capacity via offline trained deep feature representations

    更新日期:2020-11-02
  • A Simple Graph Embedding for Anomaly Detection in a Stream of Heterogeneous Labeled Graphs
    Pattern Recogn. (IF 7.196) Pub Date : 2020-11-02
    Abd Errahmane Kiouche; Sofiane Lagraa; Karima Amrouche; Hamida Seba
    更新日期:2020-11-02
  • Projected Fuzzy C-Means Clustering With Locality Preservation
    Pattern Recogn. (IF 7.196) Pub Date : 2020-11-02
    Jie Zhou; Witold Pedrycz; Xiaodong Yue; Can Gao; Zhihui Lai; Jun Wan

    Traditional partition-based clustering algorithms, hard or fuzzy version of C-means, could not deal with high-dimensional data sets effectively as redundant features may impact the computation of distances and local spatial structures among patterns are rarely considered. High dimensionality of space gives rise to so-called concentration effect that is detrimental. In this paper, a novel locality preserving

    更新日期:2020-11-02
  • Heterogeneous Ensemble Selection for Evolving Data Streams
    Pattern Recogn. (IF 7.196) Pub Date : 2020-11-02
    Anh Vu Luong; Tien Thanh Nguyen; Alan Wee-Chung Liew; Shilin Wang

    Ensemble learning has been widely applied to both batch data classification and streaming data classification. For the latter setting, most existing ensemble systems are homogenous, which means they are generated from only one type of learning model. In contrast, by combining several types of different learning models, a heterogeneous ensemble system can achieve greater diversity among its members

    更新日期:2020-11-02
  • Automatic COVID-19 Lung Infected Region Segmentation and Measurement Using CT-Scans Images
    Pattern Recogn. (IF 7.196) Pub Date : 2020-11-02
    Adel OULEFKI; Sos AGAIAN; Thaweesak TRONGTIRAKUL; Azzeddine KASSAH LAOUAR

    History shows that the infectious disease (COVID-19) can stun the world quickly, causing massive losses to health, resulting in a profound impact on the lives of billions of people, from both a safety and an economic perspective, for controlling the COVID-19 pandemic. The best strategy is to provide early intervention to stop the spread of the disease. In general, Computer Tomography (CT) is used to

    更新日期:2020-11-02
  • Imprecise Gaussian Discriminant Classification
    Pattern Recogn. (IF 7.196) Pub Date : 2020-11-01
    Yonatan Carlos Carranza Alarcón; Sébastien Destercke

    Gaussian discriminant analysis is a popular classification model, that in the precise case can produce unreliable predictions in case of high uncertainty (e.g., due to scarce or noisy data). While imprecise probability theory offers a nice theoretical framework to solve such issues, it has not been yet applied to Gaussian discriminant analysis. This work remedies this, by proposing a new Gaussian discriminant

    更新日期:2020-11-02
  • Averaging GPS segments competition 2019
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-30
    Pasi Fränti; RADU MARIESCU-ISTODOR

    Averaging GPS trajectories is needed in applications such as automatic generation of road network and finding representative movement patterns. We organized a challenge where participants submitted proposals to solve the averaging problem. In this paper, we review the proposals and evaluate their performance. We present a synthesis of the submitted methods and develop a new baseline composed of the

    更新日期:2020-11-02
  • Joint architecture and knowledge distillation in CNN for Chinese text recognition
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-25
    Zi-Rui Wang; Jun Du

    The distillation technique helps transform cumbersome neural networks into compact networks so that models can be deployed on alternative hardware devices. The main advantage of distillation-based approaches include a simple training process, supported by most off-the-shelf deep learning software and no special hardware requirements. In this paper, we propose a guideline for distilling the architecture

    更新日期:2020-10-30
  • Robust Visual Tracking via Spatio-Temporal Adaptive and Channel Selective Correlation Filters
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-29
    Yanjie Liang; Yi Liu; Yan Yan; Liming Zhang; Hanzi Wang

    In recent years, Discriminative Correlation Filter (DCF) based tracking methods have achieved impressive performance in visual tracking. However, their excellent performance usually comes at the cost of sacrificing the computational speed. Furthermore, training correlation filters using high dimensional raw features may introduce the risk of severe over-fitting. To address the above issues, we propose

    更新日期:2020-10-30
  • Enhancing In-Tree-based Clustering via Distance Ensemble and Kernelization
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-29
    Teng Qiu; Yongjie Li

    Recently, we have proposed a novel physically-inspired method, called the Nearest Descent (ND), which plays the role of organizing all the samples into an effective Graph, called the in-tree. Due to its effective characteristics, this in-tree proves very suitable for data clustering. Nevertheless, this in-tree-based clustering still has some non-trivial limitations in terms of robustness, capability

    更新日期:2020-10-30
  • A new localization method for epileptic seizure onset zones based on time-frequency and clustering analysis
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-06
    Min Wu; Ting Wan; Xiongbo Wan; Zelin Fang; Yuxiao Du

    High-frequency oscillations (HFOs) are spontaneous electroencephalogram patterns that have been regarded as potential biomarkers of epileptic seizure onset zones (SOZs). Accurately detected HFOs are used to localize SOZs, which is crucial for the presurgical assessment. Since the visual marking of HFOs is time-consuming, a method is desirable to automatically detect HFOs for localizing SOZs in clinical

    更新日期:2020-10-29
  • Robust line segment matching via reweighted random walks on the homography graph
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-08
    Dong Wei; Yongjun Zhang; Chang Li

    This paper presents a novel method for matching line segments between stereo images. Given the fundamental matrix, the local homography can be over determined with pairwise line segment candidates. We exploit this constraint to initialize the candidate and construct the novel homography graph. Because the constraint between the node is based on the epipolar geometry, the homography graph is invariant

    更新日期:2020-10-29
  • Robust semi-supervised nonnegative matrix factorization for image clustering
    Pattern Recogn. (IF 7.196) Pub Date : 2020-09-25
    Siyuan Peng; Wee Ser; Badong Chen; Zhiping Lin

    Nonnegative matrix factorization (NMF) is a powerful dimension reduction method, and has received increasing attention in various practical applications. However, most traditional NMF based algorithms are sensitive to noisy data, or fail to fully utilize the limited supervised information. In this paper, a novel robust semi-supervised NMF method, namely correntropy based semi-supervised NMF (CSNMF)

    更新日期:2020-10-29
  • Stable Feature Selection using Copula based Mutual Information
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-26
    Snehalika Lall; Debajyoti Sinha; Abhik Ghosh; Debarka Sengupta; Sanghamitra Bandyopadhyay

    Feature selection is a key step in many machine learning tasks. A majority of the existing methods of feature selection address the problem by devising some scoring function while treating the features independently, thereby overlooking their interdependencies. We leverage the scale invariance property of copula to construct a greedy, supervised feature selection algorithm that maximizes the feature

    更新日期:2020-10-29
  • Client-Specific Anomaly Detection for Face Presentation Attack Detection
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-26
    Soroush Fatemifar; Shervin Rahimzadeh Arashloo; Muhammad Awais; Josef Kittler

    One-class anomaly detection approaches are particularly appealing for use in face presentation attack detection (PAD), especially in an unseen attack scenario, where the system is exposed to novel types of attacks. This work builds upon an anomaly-based formulation of the problem and analyses the merits of deploying client-specific information for face spoofing detection. We propose training one-class

    更新日期:2020-10-29
  • A simulated annealing algorithm with a dual perturbation method for clustering
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-22
    Julian Lee; David Perkins

    Clustering is a powerful tool in exploratory data analysis that partitions a set of objects into clusters with the goal of maximizing the similarity of objects within each cluster. Due to the tendency of clustering algorithms to find suboptimal partitions of data, the approximation method Simulated Annealing (SA) has been used to search for near-optimal partitions. However, existing SA-based partitional

    更新日期:2020-10-29
  • On Parameterizing Higher-order Motion for Behaviour Recognition
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-22
    Yan Sun; Jonathon S. Hare; Mark S. Nixon

    Human behaviours consist different types of motion; we show how they can be disambiguated into their components in a richer way than that currently possible. Studies on optical flow have concentrated on motion alone without the higher order components: snap, jerk and acceleration. We are the first to show how the acceleration, jerk, snap and their constituent parts can be obtained from image sequences

    更新日期:2020-10-29
  • View-graph Construction Framework for Robust and Efficient Structure-from-Motion
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-21
    Hainan Cui; Tianxin Shi; Jun Zhang; Pengfei Xu; Yiping Meng; Shuhan Shen

    A view-graph is vital for both the accuracy and robustness of structure-from-motion (SfM). Conventional matrix decomposition techniques treat all edges of view-graph equally; hence, many edge outliers are produced in matching pairs with fewer feature matches. To address this problem, we propose an incremental framework for view-graph construction, where the robustness of matched pairs that have a larger

    更新日期:2020-10-29
  • Dynamic Spectral Residual Superpixels
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-21
    Jianchao Zhang; Angelica I. Aviles-Rivero; Daniel Heydecker; Xiaosheng Zhuang; Raymond Chan; Carola-Bibiane Schönlieb

    We consider the problem of segmenting an image into superpixels in the context of k-means clustering, in which we wish to decompose an image into local, homogeneous regions corresponding to the underlying objects. Our novel approach builds upon the widely used Simple Linear Iterative Clustering (SLIC), and incorporate a measure of objects’ structure based on the spectral residual of an image. Based

    更新日期:2020-10-29
  • CPM: A General Feature Dependency Pattern Mining Framework for Contrast Multivariate Time Series
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-21
    Qingzhe Li; Liang Zhao; Yi-Ching Lee; Avesta Sassan; Jessica Lin

    With recent advances in sensor technology, multivariate time series data are becoming extremely large with sophisticated but insightful inter-variable dependency patterns. Mining contrast dependency patterns in controlled experiments can help quantify the differences between control and experimental time series, however, overwhelms practitioners’ capability. Existing methods suffer from determining

    更新日期:2020-10-29
  • Surrogate network-based sparseness hyper-parameter optimization for deep expression recognition
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-14
    Weicheng Xie; Wenting Chen; Linlin Shen; Jinming Duan; Meng Yang

    For facial expression recognition, the sparseness constraints of the features or weights can improve the generalization ability of a deep network. However, the optimization of the hyper-parameters in fusing different sparseness strategies demands much computation, when the traditional gradient-based algorithms are used. In this work, an iterative framework with surrogate network is proposed for the

    更新日期:2020-10-17
  • Joint discriminative feature learning for multimodal finger recognition
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-14
    Shuyi Li; Bob Zhang; Lunke Fei; Shuping Zhao
    更新日期:2020-10-17
  • Temporal filtering networks for online action detection
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-10
    Hyunjun Eun; Jinyoung Moon; Jongyoul Park; Chanho Jung; Changick Kim

    Online action detection aims to detect a current action from an untrimmed, streaming video, where only current and past frames are available. Recent methods for online action detection have focused on how to model discriminative representations from temporally partial information. However, they overlook the fact that the input video contains background as well as actions. To overcome this problem,

    更新日期:2020-10-17
  • STAN: A sequential transformation attention-based network for scene text recognition
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-09
    Qingxiang Lin; Canjie Luo; Lianwen Jin; Songxuan Lai

    Scene text with an irregular layout is difficult to recognize. To this end, a Sequential Transformation Attention-based Network (STAN), which comprises a sequential transformation network and an attention-based recognition network, is proposed for general scene text recognition. The sequential transformation network rectifies irregular text by decomposing the task into a series of patch-wise basic

    更新日期:2020-10-17
  • OAENet: Oriented Attention Ensemble for Accurate Facial Expression Recognition
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-17
    Zhengning Wang; Fanwei Zeng; Shuaicheng Liu; Bing Zeng

    Facial Expression Recognition (FER) is a challenging yet important research topic owing to its significance with respect to its academic and commercial potentials. In this work, we propose an oriented attention pseudo-siamese network that takes advantage of global and local facial information for high accurate FER. Our network consists of two branches, a maintenance branch that consisted of several

    更新日期:2020-10-17
  • Reconstruction by inpainting for visual anomaly detection
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-17
    Vitjan Zavrtanik; Matej Kristan; Danijel Skčaj

    Visual anomaly detection addresses the problem of classification or localization of regions in an image that deviate from their normal appearance. A popular approach trains an auto-encoder on anomaly-free images and performs anomaly detection by calculating the difference between the input and the reconstructed image. This approach assumes that the auto-encoder will be unable to accurately reconstruct

    更新日期:2020-10-17
  • Connectivity-based Convolutional Neural Network for Classifying Point Clouds
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-17
    Jinwon Lee; Sang-Uk Cheon; Jeongsam Yang

    The acquisition of point clouds with a 3D scanner often yields large-scale, irregular, and unordered raw data, which hinders the classification of objects from these data. Some studies have introduced a method of applying the point clouds to convolutional neural networks (CNNs). This is achieved after preprocessing the volume metrics or multi-view images. However, this method has a limited resolution

    更新日期:2020-10-17
  • MetaCOVID: A Siamese neural network framework with contrastive loss for n-shot diagnosis of COVID-19 patients
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-17
    Mohammad Shorfuzzaman; M. Shamim Hossain

    Various AI functionalities such as pattern recognition and prediction can effectively be used to diagnose (recognize) and predict coronavirus disease 2019 (COVID-19) infections and propose timely response (remedial action) to minimize the spread and impact of the virus. Motivated by this, an AI system based on deep meta learning has been proposed in this research to accelerate analysis of chest X-ray

    更新日期:2020-10-17
  • Learning to transfer focus of graph neural network for scene graph parsing
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-17
    Junjie Jiang; Zaixing He; Shuyou Zhang; Xinyue Zhao; Jianrong Tan

    Scene graph parsing has become a new challenge in the field of image understanding and pattern recognition in recent years. It captures objects and their relationships, and provides a structured representation of the visual scene. Among the three types of high-level relationships of scene graphs, semantic relationships, which contain the global understanding of the scene, are the core and the most

    更新日期:2020-10-17
  • Black-box attack against handwritten signature verification with region-restricted adversarial perturbations
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-10
    Haoyang Li; Heng Li; Hansong Zhang; Wei Yuan

    Handwritten signature verification is used to verify the identity of individuals through recognizing their signatures. Adversarial examples can induce misclassification, hence posing a severe threat to signature verification. At present, a variety of adversarial example attacks have been developed for image classification, but they are not that useful for attacking signature verification due to two

    更新日期:2020-10-16
  • Enhanced low-rank constraint for temporal subspace clustering and its acceleration scheme
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-01
    Jianwei Zheng; Ping Yang; Guojiang Shen; Shengyong Chen; Wei Zhang

    Inspired by the temporal subspace clustering (TSC) method and low-rank matrix approximation constraint, a new model is proposed termed as temporal plus low-rank subspace clustering (TLRSC) by utilizing both the local and global structural information. On one hand, to solve the drawback that the nuclear norm-based constraint usually results in a suboptimal solution, we incorporate certain nonconvex

    更新日期:2020-10-16
  • Hypergraph video pedestrian re-identification based on posture structure relationship and action constraints
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-08
    Xiaoqiang Hu; Dan Wei; Ziyang Wang; Jianglin Shen; Hongjuan Ren

    Discriminative feature learning is critical for pedestrian re-identification. Previous part-based methods mainly focus on the region of specific predefined semantics to learn local representations, ignoring the influence of posture changes, and the learning efficiency and robustness in complex scenes are poor. In this paper, a hypergraph video pedestrian re-identification method based on posture structure

    更新日期:2020-10-16
  • KDD: A kernel density based descriptor for 3D point clouds
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-08
    Yuhe Zhang; Chunhui Li; Bao Guo; Chenhao Guo; Shunli Zhang

    3D feature description is one of the central techniques that rely on point clouds since a lot of point cloud processing techniques apply the point-to-point correspondences that are achieved via feature descriptors as input data. The feature descriptor encodes the information of the underlying surface around the feature point so as to make a local surface distinguished from another. The focus of the

    更新日期:2020-10-16
  • Multicamera Pedestrian Detection Using Logic Minimization
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-14
    Yuyao Yan; Ming Xu; Jeremy S. Smith; Mo Shen; Jin Xi

    In this paper an algorithm for multicamera pedestrian detection is proposed. The first stage of this work is based on the probabilistic occupancy map framework, in which the ground plane is discretized into a grid and the likelihood of pedestrian presence at each location is estimated by comparing a rectangle, of the average size of the pedestrians standing there, with the foreground silhouettes in

    更新日期:2020-10-15
  • Time-series averaging and local stability-weighted dynamic time warping for online signature verification
    Pattern Recogn. (IF 7.196) Pub Date : 2020-10-14
    Manabu Okawa

    To meet the recent demands for automated security systems, this study proposes a novel single-template strategy that uses mean templates and local stability-weighted dynamic time warping (LS-DTW) to simultaneously improve the speed and accuracy of online signature verification. Specifically, we adopt a recent time-series averaging method, called Euclidean barycenter-based DTW barycenter averaging (EB-DBA)

    更新日期:2020-10-15
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