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  • Accelerating information entropy-based feature selection using rough set theory with classified nested equivalence classes
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-25
    Jie Zhao; Jia-ming Liang; Zhen-ning Dong; De-yu Tang; Zhen Liu

    Feature selection effectively reduces the dimensionality of data. For feature selection, rough set theory offers a systematic theoretical framework based on consistency measures, of which information entropy is one of the most important significance measures of attributes. However, an information-entropy-based significance measure is computationally expensive and requires repeated calculations. Although

    更新日期:2020-07-07
  • Self-supervised deep reconstruction of mixed strip-shredded text documents
    Pattern Recogn. (IF 7.196) Pub Date : 2020-07-03
    Thiago M. Paixão; Rodrigo F. Berriel; Maria C.S. Boeres; Alessandro L. Koerich; Claudine Badue; Alberto F. De Souza; Thiago Oliveira-Santos

    The reconstruction of shredded documents consists of coherently arranging fragments of paper (shreds) to recover the original document(s). A great challenge in computational reconstruction is to properly evaluate the compatibility between the shreds. While traditional pixel-based approaches are not robust to real shredding, more sophisticated solutions compromise significantly time performance. The

    更新日期:2020-07-07
  • Sparse regularized low-rank tensor regression with applications in genomic data analysis
    Pattern Recogn. (IF 7.196) Pub Date : 2020-07-01
    Le Ou-Yang; Xiao-Fei Zhang; Hong Yan

    Many applications in biomedical informatics deal with data in the tensor form. Traditional regression methods which take vectors as covariates may encounter difficulties in handling tensors due to their ultrahigh dimensionality and complex structure. In this paper, we introduce a novel sparse regularized Tucker tensor regression model to exploit the structure of tensor covariates and perform feature

    更新日期:2020-07-07
  • Counter-examples generation from a positive unlabeled image dataset
    Pattern Recogn. (IF 7.196) Pub Date : 2020-07-01
    Florent Chiaroni; Ghazaleh Khodabandelou; Mohamed-Cherif Rahal; Nicolas Hueber; Frederic Dufaux

    This paper considers the problem of positive unlabeled (PU) learning. In this context, we propose a two-stage GAN-based model. More specifically, the main contribution is to incorporate a biased PU risk within the standard GAN discriminator loss function. In this manner, the discriminator is constrained to steer the generator to converge towards the unlabeled samples distribution while diverging from

    更新日期:2020-07-07
  • Gesture recognition based on deep deformable 3D convolutional neural networks
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-24
    Yifan Zhang; Lei Shi; Yi Wu; Ke Cheng; Jian Cheng; Hanqing Lu

    Dynamic gesture recognition, which plays an essential role in human-computer interaction, has been widely investigated but not yet fully addressed. The challenge mainly lies in three folders: 1) to model both of the spatial appearance and the temporal evolution simultaneously; 2) to address the interference from the varied and complex background; 3) the requirement of real-time processing. In this

    更新日期:2020-07-07
  • Multi-scale Deep Relational Reasoning for Facial Kinship Verification
    Pattern Recogn. (IF 7.196) Pub Date : 2020-07-06
    Haibin Yan; Chaohui Song

    In this paper, we propose a deep relational network which exploits multi-scale information of facial images for kinship verification. Unlike most existing deep learning based facial kinship verification methods which employ convolutional neural networks to extract holistic features, we present a deep model to exploit facial kinship relationship from local regions. For each given pair of face images

    更新日期:2020-07-07
  • HCNN-PSI: A Hybrid CNN with Partial Semantic Information for Space Target Recognition
    Pattern Recogn. (IF 7.196) Pub Date : 2020-07-06
    Xi Yang; Tan Wu; Nannan Wang; Yan Huang; Bin Song; Xinbo Gao

    Space target recognition is the basic task of space situational awareness and has developed significantly in the last decade. This paper proposes a hybrid convolutional neural network with partial semantic information for space target recognition, which joints the global features and partial semantic information. Firstly, we propose a two-stage target detection network based on the characteristics

    更新日期:2020-07-07
  • Challenging Tough Samples in Unsupervised Domain Adaptation
    Pattern Recogn. (IF 7.196) Pub Date : 2020-07-06
    Lin Zuo; Mengmeng Jing; Jingjing Li; Lei Zhu; Ke Lu; Yang Yang

    Existing domain adaptation approaches focus on taking advantage of easy samples, i.e, target samples which are easier for adaptation. In previous work, tough, or hard, target samples are generally regarded as outliers or just being left to chance. As a result, the adaptation of tough target samples remains as a challenging problem in the community. In this paper, we report three novel ideas for domain

    更新日期:2020-07-07
  • Complex heterogeneity learning: A theoretical and empirical study
    Pattern Recogn. (IF 7.196) Pub Date : 2020-07-02
    Pei Yang; Qi Tan; Jingrui He

    Data heterogeneity such as task heterogeneity, view heterogeneity, and instance heterogeneity often co-exist in many real-world applications including insider threat detection, traffic prediction, brain image analysis, quality control in manufacturing processes, etc. However, most of the existing techniques might not take fully advantage of the rich heterogeneity. To address this problem, we propose

    更新日期:2020-07-06
  • The benefits of target relations: A comparison of multitask extensions and classifier chains
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-24
    Esra Adıyeke; Mustafa Gökçe Baydoğan

    Multitask (multi-target or multi-output) learning (MTL) deals with simultaneous prediction of several outputs. MTL approaches rely on the optimization of a joint score function over the targets. However, defining a joint score in global models is problematic when the target scales are different. To address such problems, single target (i.e. local) learning strategies are commonly employed. Here we

    更新日期:2020-07-06
  • Modality Adversarial Neural Network for Visible-Thermal Person Re-identification
    Pattern Recogn. (IF 7.196) Pub Date : 2020-07-06
    Yi Hao; Jie Li; Nannan Wang; Xinbo Gao

    Existing Visible-Thermal Person Re-identification (VT-REID) methods usually adopt two-stream networks for cross-modality images. The two streams are trained to extract features from different modality images respectively. In contrast, we design a Modality Adversarial Neural Network (MANN) to solve VT-REID problem. Our proposed MANN includes a one-stream feature extractor and a modality discriminator

    更新日期:2020-07-06
  • A linear multivariate binary decision tree classifier based on K-means splitting
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-24
    Fei Wang; Quan Wang; Feiping Nie; Zhongheng Li; Weizhong Yu; Fuji Ren

    A novel linear multivariate decision tree classifier, Binary Decision Tree based on K-means Splitting (BDTKS), is presented in this paper. The unsupervised K-means clustering is recursively integrated into the binary tree, building a hierarchical classifier. The introduction of the unsupervised K-means clustering provides the powerful generalization ability for the resulting BDTKS model. Then, the

    更新日期:2020-07-05
  • Dirichlet Variational Autoencoder
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-24
    Weonyoung Joo; Wonsung Lee; Sungrae Park; Il-Chul Moon

    This paper proposes Dirichlet Variational Autoencoder (DirVAE) using a Dirichlet prior. To infer the parameters of DirVAE, we utilize the stochastic gradient method by approximating the inverse cumulative distribution function of the Gamma distribution, which is a component of the Dirichlet distribution. This approximation on a new prior led an investigation on the component collapsing, and DirVAE

    更新日期:2020-07-05
  • A novel strategy to balance the results of cross-modal hashing
    Pattern Recogn. (IF 7.196) Pub Date : 2020-07-01
    Fangming Zhong; Zhikui Chen; Geyong Min; Feng Xia

    Hashing methods for cross-modal retrieval has drawn increasing research interests and has been widely studied in recent years due to the explosive growth of multimedia big data. However, a significant phenomenon which has been ignored is that there is a large gap between the results of cross-modal hashing in most cases. For example, the results of Text-to-Image frequently outperform that of Image-to-Text

    更新日期:2020-07-05
  • Constructing Multilayer Locality-Constrained Matrix Regression Framework for Noise Robust Face Super-Resolution
    Pattern Recogn. (IF 7.196) Pub Date : 2020-07-04
    Guangwei Gao; Yi Yu; Jin Xie; Jian Yang; Meng Yang; Jian Zhang

    Representation learning methods have attracted considerable attention in learning-based face super-resolution in recent years. Conventional methods perform local models learning on low-resolution (LR) manifold and face reconstruction on high-resolution (HR) manifold respectively, leading to unsatisfactory reconstruction performance when the acquired LR face images are severely degraded (e.g., noisy

    更新日期:2020-07-05
  • Spectral Clustering via Ensemble Deep Autoencoder Learning (SC-EDAE)
    Pattern Recogn. (IF 7.196) Pub Date : 2020-07-03
    Séverine Affeldt; Lazhar Labiod; Mohamed Nadif
    更新日期:2020-07-03
  • Graph-Based Parallel Large Scale Structure from Motion
    Pattern Recogn. (IF 7.196) Pub Date : 2020-07-03
    Yu Chen; Shuhan Shen; Yisong Chen; Guoping Wang

    While Structure from Motion achieves great success in 3D reconstruction, it still meets challenges on large scale scenes. Incremental SfM approaches are robust to outliers, but are limited by low efficiency and easy suffer from drift problem. Though Global SfM methods are more efficient than incremental approaches, they are sensitive to outliers, and would also meet memory limitation and time bottleneck

    更新日期:2020-07-03
  • Multi-head enhanced self-attention network for novelty detection
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-06
    Yingying Zhang; Yuxin Gong; Haogang Zhu; Xiao Bai; Wenzhong Tang

    One-class classification (OCC) is a classical problem in computer vision that can be described as the task of classifying outlier class samples (OC samples) from the OCC model trained on inlier class samples (IC samples) when datasets are highly biased toward one class due to the insufficient sample size of the other class. Currently, the adversarial learning OCC (ALOCC) method has been proven to significantly

    更新日期:2020-07-02
  • Discovering and Incorporating Latent Target-Domains for Domain Adaptation
    Pattern Recogn. (IF 7.196) Pub Date : 2020-07-02
    Haoliang Li; Wen Li; Shiqi Wang

    In this paper, we aim to address the unsupervised domain adaptation problem where the data in the target domain are much more diverse compared with the data in the source domain. In particular, this problem is formulated as discovering and incorporating latent domains underlying target data of interest for unsupervised domain adaptation. More specifically, the discovery of the latent target domains

    更新日期:2020-07-02
  • Fast and Incremental Algorithms for Exponential Semi-Supervised Discriminant Embedding
    Pattern Recogn. (IF 7.196) Pub Date : 2020-07-02
    Yingdi Lu; Gang Wu

    In various pattern classification problems, semi-supervised learning methods have shown its effectiveness in utilizing unlabeled data to yield better performance than some supervised and unsupervised learning methods. Semi-supervised discriminant embedding (SDE) is a semi-supervised extension of local discriminant embedding (LDE). However, when dealing with high dimensional data, SDE often suffers

    更新日期:2020-07-02
  • Single-shot 3D multi-person pose estimation in complex images
    Pattern Recogn. (IF 7.196) Pub Date : 2020-07-02
    Abdallah Benzine; Bertrand Luvison; Quoc Cuong Pham; Catherine Achard

    In this paper, we propose a new single shot method for multi-person 3D human pose estimation in complex images. The model jointly learns to locate the human joints in the image, to estimate their 3D coordinates and to group these predictions into full human skeletons. The proposed method deals with a variable number of people and does not need bounding boxes to estimate the 3D poses. It leverages and

    更新日期:2020-07-02
  • Two-stage Knowledge Transfer Framework for Image Classification
    Pattern Recogn. (IF 7.196) Pub Date : 2020-07-02
    Jianhang Zhou; Shaoning Zeng; Bob Zhang

    The two-stage strategy has been widely used in image classification. However, these methods barely take the classification criteria of the first stage into consideration in the second prediction stage. In this paper, we propose a novel Two-Stage Representation method (TSR), and convert it to a Single-Teacher Single-Student (STSS) problem in our two-stage knowledge transfer framework for image classification

    更新日期:2020-07-02
  • Image segmentation using dense and sparse hierarchies of superpixels
    Pattern Recogn. (IF 7.196) Pub Date : 2020-07-02
    Felipe Lemes Galvão; Silvio Jamil Ferzoli Guimarães; Alexandre Xavier Falcão

    We investigate the intersection between hierarchical and superpixel image segmentation. Two strategies are considered: (i) the classical region merging, that creates a dense hierarchy with a higher number of levels, and (ii) the recursive execution of some superpixel algorithm, which generates a sparse hierarchy with fewer levels. We show that, while dense methods can capture more intermediate or higher-level

    更新日期:2020-07-02
  • Active contour model for inhomogenous image segmentation based on Jeffreys divergence
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-25
    Bin Han; Yiquan Wu

    Inhomogenous image segmentation has been a research challenge in recent years. To deal with this difficulty, we propose a new local and global active contour model based on Jeffreys divergence. First, unlike the local data fitting energy of the region-scalable fitting model, a new local data fitting energy based on Jeffreys divergence is proposed instead of Euclidean distance, which achieves relatively

    更新日期:2020-07-01
  • Dynamic graph convolutional network for multi-video summarization
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-20
    Jiaxin Wu; Sheng-hua Zhong; Yan Liu

    Multi-video summarization is an effective tool for users to browse multiple videos. In this paper, multi-video summarization is formulated as a graph analysis problem and a dynamic graph convolutional network is proposed to measure the importance and relevance of each video shot in its own video as well as in the whole video collection. Two strategies are proposed to solve the inherent class imbalance

    更新日期:2020-07-01
  • One-vs-One Classification for Deep Neural Networks
    Pattern Recogn. (IF 7.196) Pub Date : 2020-07-01
    Pornntiwa Pawara; Emmanuel Okafor; Marc Groefsema; Sheng He; Lambert R.B. Schomaker; Marco A. Wiering

    For performing multi-class classification, deep neural networks almost always employ a One-vs-All (OvA) classification scheme with as many output units as there are classes in a dataset. The problem of this approach is that each output unit requires a complex decision boundary to separate examples from one class from all other examples. In this paper, we propose a novel One-vs-One (OvO) classification

    更新日期:2020-07-01
  • A Generalized Weighted Distance k-Nearest Neighbor for Multi-label Problems
    Pattern Recogn. (IF 7.196) Pub Date : 2020-07-01
    Niloofar Rastin; Mansoor Zolghadri Jahromi; Mohammad Taheri

    In multi-label classification, each instance is associated with a set of pre-specified labels. One common approach is to use Binary Relevance (BR) paradigm to learn each label by a base classifier separately. Use of k-Nearest Neighbor (kNN) as the base classifier (denoted as BRkNN) is a simple, descriptive and powerful approach. In binary relevance a highly imbalanced view of dataset is used. However

    更新日期:2020-07-01
  • Accurate, Data-Efficient, Unconstrained Text Recognition with Convolutional Neural Networks
    Pattern Recogn. (IF 7.196) Pub Date : 2020-07-01
    Mohamed Yousef; Khaled F. Hussain; Usama S. Mohammed

    Unconstrained text recognition is an important computer vision task, featuring a wide variety of different sub-tasks, each with its own set of challenges. One of the biggest promises of deep neural networks has been the convergence and automation of feature extractors from input raw signals, allowing for the highest possible performance with minimum required domain knowledge. To this end, we propose

    更新日期:2020-07-01
  • Distractor-aware discrimination learning for online multiple object tracking
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-24
    Zongwei Zhou; Wenhan Luo; Qiang Wang; Junliang Xing; Weiming Hu

    Online multi-object tracking needs to overcome the intrinsic detector deficiencies, e.g., missing detections, false alarms, and inaccurate detection responses, to grow multiple object trajectories without using future information. Various distractions exist during this growing process like background clutters, similar targets, and occlusions, which present a great challenge. We in this work propose

    更新日期:2020-06-30
  • Nonlinear dimensionality reduction for clustering
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-19
    Sotiris Tasoulis; Nicos G. Pavlidis; Teemu Roos

    We introduce an approach to divisive hierarchical clustering that is capable of identifying clusters in nonlinear manifolds. This approach uses the isometric mapping (Isomap) to recursively embed (subsets of) the data in one dimension, and then performs a binary partition designed to avoid the splitting of clusters. We provide a theoretical analysis of the conditions under which contiguous and high-density

    更新日期:2020-06-29
  • Fast sparse coding networks for anomaly detection in videos
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-24
    Peng Wu; Jing Liu; Mingming Li; Yujia Sun; Fang Shen

    The semi-supervised video anomaly detection assumes that only normal video clips are available for training. Therefore, the intuitive idea is either to learn a dictionary by sparse coding or to train encoding-decoding neural networks by minimizing the reconstruction errors. For the former, the optimization of sparse coefficients is extremely time-consuming. For the latter, this manner cannot guarantee

    更新日期:2020-06-29
  • Depth image super-resolution using correlation-controlled color guidance and multi-scale symmetric network
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-24
    Tao Li; Hongwei Lin; Xiucheng Dong; Xiaohua Zhang

    Depth image super-resolution (DISR) is an effective solution to improve the quality of depth images captured by real world low-cost cameras. In this paper, we propose a multi-scale symmetric network with the correlation-controlled color guidance block (CCGB) for DISR. The proposed network consists of two multi-scale sub-networks to respectively provide guidance and estimate depth. A symmetric unit

    更新日期:2020-06-29
  • On the stability of persistent entropy and new summary functions for topological data analysis
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-18
    Nieves Atienza; Rocio Gonzalez-Díaz; Manuel Soriano-Trigueros

    Persistent homology and persistent entropy have recently become useful tools for patter recognition. In this paper, we find requirements under which persistent entropy is stable to small perturbations in the input data and scale invariant. In addition, we describe two new stable summary functions combining persistent entropy and the Betti curve. Finally, we use the previously defined summary functions

    更新日期:2020-06-25
  • Low-rank quaternion tensor completion for recovering color videos and images
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-19
    Jifei Miao; Kit Ian Kou; Wankai Liu

    Low-rank quaternion tensor completion method, a novel approach to recovery color videos and images, is proposed in this paper. We respectively reconstruct a color image and a color video as a quaternion matrix (second-order tensor) and a third-order quaternion tensor by encoding the red, green, and blue channel pixel values on the three imaginary parts of a quaternion. Different from some traditional

    更新日期:2020-06-25
  • Efficient sampling-based energy function evaluation for ensemble optimization using simulated annealing
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-19
    János Tóth; Henrietta Tomán; András Hajdu

    In this study, we attempted to develop a method for accelerating parameter optimization of an object detector ensemble over large image datasets by using simulated annealing. We propose a novel sampling-based evaluation method that considers the minimum portion of the dataset required in each iteration to maintain solution quality. This approach can be considered a noisy evaluation of the energy. The

    更新日期:2020-06-25
  • Handling incomplete heterogeneous data using VAEs
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-13
    Alfredo Nazábal; Pablo M. Olmos; Zoubin Ghahramani; Isabel Valera

    Variational autoencoders (VAEs), as well as other generative models, have been shown to be efficient and accurate for capturing the latent structure of vast amounts of complex high-dimensional data. However, existing VAEs can still not directly handle data that are heterogenous (mixed continuous and discrete) or incomplete (with missing data at random), which is indeed common in real-world applications

    更新日期:2020-06-25
  • On Unsupervised Simultaneous Kernel Learning and Data Clustering
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-24
    Akshay Malhotra; Ioannis D. Schizas

    A novel optimization framework for joint unsupervised clustering and kernel learning is derived. Sparse nonnegative matrix factorization of kernel covariance matrices is utilized to categorize data according to their information content. It is demonstrated that a pertinent kernel covariance matrix for clustering can be constructed such that it is block diagonal within arbitrary row and column permutations

    更新日期:2020-06-24
  • A sparsity-promoting image decomposition model for depth recovery
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-13
    Xinchen Ye; Mingliang Zhang; Jingyu Yang; Xin Fan; Fangfang Guo

    This paper proposes a novel image decomposition model for scene depth recovery from low-quality depth measurements and its corresponding high resolution color image. Through our observation, the depth map mainly contains smooth regions separated by additive step discontinuities, and can be simultaneously decomposed into a local smooth surface and an approximately piecewise constant component. Therefore

    更新日期:2020-06-23
  • A divide-and-conquer strategy for facial landmark detection using dual-task CNN architecture
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-13
    Rachida Hannane; Abdessamad Elboushaki; Karim Afdel

    In this paper, we propose a novel deep learning-based framework for facial landmark detection. This framework takes as input face image returned by a face detector (Faster R-CNN) and generates as output a set of landmarks positions. Prior CNN-based methods often select randomly small local patches to predict an initial guess of landmarks locations. One issue with these local patches is that the adjacent

    更新日期:2020-06-23
  • Multi-Label classification of multi-modality skin lesion via hyper-connected convolutional neural network
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-18
    Lei Bi; David Dagan Feng; Michael Fulham; Jinman Kim

    Objective Clinical and dermoscopy images (multi-modality image pairs) are routinely used sequentially in the assessment of skin lesions. Clinical images characterize a lesion's geometry and color; dermoscopy depicts vascularity, dots and globules from the sub-surface of the lesion. Together these modalities provide labels to characterize a skin lesion. Recently, convolutional neural networks (CNNs)

    更新日期:2020-06-23
  • Zero-shot Handwritten Chinese Character Recognition with hierarchical decomposition embedding
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-06
    Zhong Cao; Jiang Lu; Sen Cui; Changshui Zhang

    Handwritten Chinese Character Recognition (HCCR) is a challenging topic in the field of pattern recognition due to large-scale character vocabulary, complex hierarchical structure, various writing styles, and scarce training samples. In this paper, we explored the hierarchical knowledge of Chinese characters and presented a novel zero-shot HCCR method. First, we handled the relations between the characters

    更新日期:2020-06-23
  • Precise detection of Chinese characters in historical documents with deep reinforcement learning
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-18
    Wu Sihang; Wang Jiapeng; Ma Weihong; Jin Lianwen

    The decision-making ability of deep reinforcement learning has been proved successfully in a variety of fields. Here, we use this method for precise character detection by making tight bounding boxes around the Chinese characters in historical documents. An agent is trained to learn the control policy of fine-tuning a bounding box step-by-step through a Markov Decision Process. We introduce a novel

    更新日期:2020-06-23
  • A robust matching pursuit algorithm using information theoretic learning
    Pattern Recogn. (IF 7.196) Pub Date : 2020-05-25
    Miaohua Zhang; Yongsheng Gao; Changming Sun; Michael Blumenstein

    Current orthogonal matching pursuit (OMP) algorithms calculate the correlation between two vectors using the inner product operation and minimize the mean square error, which are both suboptimal when there are non-Gaussian noises or outliers in the observation data. To overcome these problems, a new OMP algorithm is developed based on information theoretic learning (ITL), which is built on the following

    更新日期:2020-06-23
  • Cost-sensitive deep forest for price prediction
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-15
    Chao Ma; Zhenbing Liu; Zhiguang Cao; Wen Song; Jie Zhang; Weiliang Zeng

    For many real-world applications, predicting a price range is more practical and desirable than predicting a concrete value. In this case, price prediction can be regarded as a classification problem. Although deep forest is recognized as the best solution to many classification problems, a crucial issue limits its direct application to price prediction, i.e., it treated all the misclassifications

    更新日期:2020-06-23
  • Deep Multi-task Learning with Relational Attention for Business Success Prediction
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-20
    Jiejie Zhao; Bowen Du; Leilei Sun; Weifeng Lv; Yanchi Liu; Hui Xiong

    Multi-task learning is a promising machine learning branch, which aims to improve the generalization of the prediction models by sharing knowledge among tasks. Most of the existing multi-task learning methods rely on predefined task relationships and guide the learning process of models by linear regularization terms. On the one hand, improper setting of task relationships may result in negative knowledge

    更新日期:2020-06-23
  • Self-adaptive manifold discriminant analysis for feature extraction from hyperspectral imagery
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-12
    Hong Huang; Zhengying Li; Haibo He; Yule Duan; Song Yang

    Traditional manifold learning methods generally include a single projection stage that maps high-dimensional data into lower-dimensional space. However, these methods cannot guarantee that the projection matrix is optimal for classification, which limits their practical application. To address this issue, we propose a two-stage projection matrix optimization model termed self-adaptive manifold discriminant

    更新日期:2020-06-19
  • Skeleton-Based Action Recognition with Hierarchical Spatial Reasoning and Temporal Stack Learning Network
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-19
    Chenyang Si; Ya Jing; Wei Wang; Liang Wang; Tieniu Tan

    Skeleton-based action recognition aims to recognize human actions by exploring the inherent characteristics from the given skeleton sequences and has attracted far more attention due to its great important potentials in practical applications. Previous methods have illustrated that learning discriminative spatial and temporal features from the skeleton sequences is a crucial factor to recognize human

    更新日期:2020-06-19
  • Semantic segmentation using stride spatial pyramid pooling and dual attention decoder
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-13
    Chengli Peng; Jiayi Ma

    Semantic segmentation is an end-to-end task that requires both semantic and spatial accuracy. It is important for deep learning-based segmentation methods to effectively utilize the high-level feature map whose semantic information is abundant and the low-level feature map whose spatial information is accurate. However, existing segmentation networks typically cannot take full advantage of these two

    更新日期:2020-06-18
  • Semi-supervised learning framework based on statistical analysis for image set classification
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-13
    Wenzhu Yan; Quansen Sun; Huaijiang Sun; Yanmeng Li

    Statistical models have been widely adopted for image set classification owing to their capacity in characterizing the data distribution more flexibly and faithfully. However, these methods typically suffer from the problem that the query image set has weak statistical correlations with the training sets, which leads to larger fluctuations in performance. To address this problem, we propose a semi-supervised

    更新日期:2020-06-18
  • Visual tracking by dynamic matching-classification network switching
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-01
    Peixia Li; Boyu Chen; Dong Wang; Huchuan Lu

    Existing deep trackers can be roughly divided into either matching-based or classification-based methods. The formers are fast but not very robust; while the latter ones introduce more discriminative information but often very slow. In this work, we present a novel real-time robust tracking method to take full use of the benefits from both kinds of networks. First, we propose a matching-classification

    更新日期:2020-06-18
  • Defocus map estimation from a single image using improved likelihood feature and edge-based basis
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-06
    Shaojun Liu; Qingmin Liao; Jing-Hao Xue; Fei Zhou

    Defocus map estimation (DME) is very useful in many computer vision applications and has drawn much attention in recent years. Edge-based DME methods can generate sharp defocus discontinuities but usually suffer from textures of the input image. Region-based methods are free of textures but cannot catch the defocus discontinuities very well. In this paper, we propose a DME method combining edge-based

    更新日期:2020-06-06
  • Play and rewind: Context-aware video temporal action proposals
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-06
    Lianli Gao; Tao Li; Jingkuan Song; Zhou Zhao; Heng Tao Shen

    In this paper, we investigate the problem of Temporal Action Proposal (TAP) generation, which plays a fundamental role in large-scale untrimmed video analysis but remains largely unsolved. Most of the prior works proposed the temporal actions by predicting the temporal boundaries or actionness scores of video units. Nevertheless, context information among surrounding video units has not been adequately

    更新日期:2020-06-06
  • Generative attention adversarial classification network for unsupervised domain adaptation
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-05
    Wendong Chen; Haifeng Hu

    Domain adaptation is a significant and popular issue of solving distribution discrepancy among different domains in computer vision. Generally, previous works proposed are mainly devoted to reducing domain shift between source domain with labeled data and target domain without labels. Adversarial learning in deep networks has already been widely applied to learn disentangled and transferable features

    更新日期:2020-06-05
  • Attention and boundary guided salient object detection
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-05
    Qing Zhang; Yanjiao Shi; Xueqin Zhang

    In recent years, fully convolutional neural network (FCN) has broken all records in various vision task. It also achieves great performance in salient object detection. However, most of the state-of-the-art methods have suffered from the challenge of precisely segmenting the entire salient object with uniform region and explicit boundary and effectively suppressing the backgrounds on complex images

    更新日期:2020-06-05
  • Enhanced automatic twin support vector machine for imbalanced data classification
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-03
    C. Jimenez-Castaño; A. Alvarez-Meza; A. Orozco-Gutierrez
    更新日期:2020-06-03
  • Towards Non-I.I.D. Image Classification: A Dataset and Baselines
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-02
    Yue He; Zheyan Shen; Peng Cui

    I.I.D.5 hypothesis between training and testing data is the basis of numerous image classification methods. Such property can hardly be guaranteed in practice where the Non-IIDness is common, causing instable performances of these models. In literature, however, the Non-I.I.D.6 image classification problem is largely understudied. A key reason is lacking of a well-designed dataset to support related

    更新日期:2020-06-02
  • Adaptive core fusion-based density peak clustering for complex data with arbitrary shapes and densities
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-02
    Fang Fang; Lei Qiu; Shenfang Yuan

    A challenging issue of clustering in real-word application is to detect clusters with arbitrary shapes and densities in complex data. Many conventional clustering algorithms are capable of detecting non-spherical clusters, but their performance is limited when processing data with complex shapes and multiple density peaks in a cluster without knowing the number of clusters. This paper proposes an adaptive

    更新日期:2020-06-02
  • Neural network with multiple connection weights
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-02
    Jiangshe Zhang; Junying Hu; Junmin Liu

    Biological studies have shown that the interaction between neurons are based on neurotransmitters, which transmit signals between neurons, and that one neuron sends information to another neuron by releasing a number of different neurotransmitters, which play different roles. Motivated by this biological discovery, a novel neural networks model is proposed by extending the dimension of connection weights

    更新日期:2020-06-02
  • 3DSymm: Robust and Accurate 3D Reflection Symmetry Detection
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-01
    Rajendra Nagar; Shanmuganathan Raman

    Reflection symmetry is a very commonly occurring feature in both natural and man-made objects, which helps in understanding objects better and makes them visually pleasing. Detection of reflection symmetry is a fundamental problem in the field of computer vision and computer graphics which aids in understanding and representing reflective symmetric objects. In this work, we attempt the problem of detecting

    更新日期:2020-06-01
  • Learning Spatial-Temporal Deformable Networks for Unconstrained Face Alignment and Tracking in Videos
    Pattern Recogn. (IF 7.196) Pub Date : 2020-06-01
    Hongyu Zhu; Hao Liu; Congcong Zhu; Zongyong Deng; Xuehong Sun

    In this paper, we propose a spatial-temporal deformable networks approach to investigate both problems of face alignment in static images and face tracking in videos under unconstrained environments. Unlike conventional feature extractions which cannot explicitly exploit augmented spatial geometry for various facial shapes, in our approach, we propose a deformable hourglass networks (DHGN) method,

    更新日期:2020-06-01
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