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24th international Conference on pattern recognition (ICPR)

作者： Cui, Lixin Bai, Lu Rossi, Luca Wang, Yue Jiao, Yuhang Hancock, Edwin R. Cent Univ Finance & Econ 39 South Coll Rd Beijing Peoples R China Aston Univ Sch Engn & Appl Sci Birmingham B4 7ET W Midlands England Univ York Dept Comp Sci York YO10 5DD N Yorkshire England

ISBN: (纸本)9781538637883

In this paper, we develop a new deep hybrid graph kernel. this is based on the depth-based matching kernel [1] and the Weisfeiler-Lehman subtree kernel [2], by jointly computing a basic deep kernel that simultaneously captures the relationship between the combined kernels through deep learning networks. Specifically, for a set of graphs under investigations, we commence by computing two kernel matrices using each of the separate kernels. With the two kernel matrices to hand, for each graph we use the kernel value between the graph and each of the training graphs as the graph characterisation vector. this vector can be seen as a kernel-based similarity embedding vector of the graph [3]. We use the embedding vectors of all graphs to train a deep autoencoder network, that is optimized using Stochastic Gradient Descent together with the Deep Belief Network for pretraining. the deep representation computed through the deep learning network captures the main relationship between the depth-based matching kernel and the Weisfeiler-Lehman subtree kernel. the resulting deep hybrid graph kernel is computed by summing the original kernels together with the dot product kernel between their deep representations. We show that the deep hybrid graph kernel not only captures the joint information between the associated depth-based matching and Weisfeiler-Lehman subtree kernels, but also reflects the information content over all graphs under investigations. Experimental evaluations demonstrate the effectiveness of the proposed kernel.

关键词： Kernel Decoding Training Tools Convolution Reliability

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10th IAPR-TC-15 international workshop on graph-based representations in pattern recognition, GbRPR 2015

10th IAPR-TC-15 International Workshop on Graph-Based Repres...

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10th IAPR-TC-15 international workshop on graph-based representations in pattern recognition, GbRPR 2015

ISBN: (纸本)9783319182230

the proceedings contain 36 papers. the special focus in this conference is on graph-based Representation, graph Matching, graph Clustering and graph-based Applications. the topics include: Approximation of graph edit distance in quadratic time;data graph formulation as the minimum-weight maximum-entropy problem;an entropic edge assortativity measure;a subpath kernel for learning hierarchical image representations;coupled-feature hypergraph representation for feature selection;reeb graphs through local binary patterns;incremental embedding within a dissimilarity-based framework;a first step towards exact graph edit distance using bipartite graph matching;consensus of two graph correspondences through a generalisation of the bipartite graph matching;revisiting Volgenant-Jonker for approximating graph edit distance;a hypergraph matching framework for refining multi-source feature correspondences;a tool for solving substitution-tolerant subgraph isomorphism;a graph database repository and performance evaluation metrics for graph edit distance;learning graph model for different dimensions image matching;report on the first contest on graph matching algorithms for pattern search in biological databases;large-scale graph indexing using binary embeddings of node contexts;on the influence of node centralities on graph edit distance for graph classification;a quantum Jensen-Shannon graph kernel using discrete-time quantum walks;density based cluster extension and dominant sets clustering;salient object segmentation from stereoscopic images;causal video segmentation using superseeds and graph matching;fast minimum spanning tree based clustering algorithms on local neighborhood graph and graph based lymphatic vessel wall localisation and tracking.

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Low-Resolution Face recognition with Deep Convolutional Features in the Dissimilarity Space 6th

Low-Resolution Face Recognition with Deep Convolutional Feat...

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6th international workshop on Artificial Intelligence and pattern recognition (IWAIPR)

作者： Hernandez-Duran, Mairelys Plasencia-Calana, Yenisel Mendez-Vazquez, Heydi Adv Technol Applicat Ctr 7ma A 21406 Havana Cuba

ISBN: (纸本)9783030011321;9783030011314

In video surveillance and others real-life applications, it is usually needed to match low resolution (LR) face images against high-resolution (HR) gallery images. Although extensive efforts have been made, it is still difficult to find effective representations for low-resolution face recognition due to the degradation in resolution together with facial variations. this paper makes use of alternative representations based on dissimilarities between objects. Unlike previous works, we construct the dissimilarity space on top of deep convolutional features. We obtain a more compact representation by using prototype selection methods. Besides, metric learning methods are used to replace the standard Euclidean distance in the dissimilarity space. Experiments conducted on two data sets particularly designed for low-resolution face recognition showed that the proposal outperforms state-of-the-art methods, including some neural networks designed for this problem.

关键词： Face recognition Low-resolution Dissimilarity representation Convolutional networks

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Exploring Local Deep representations for Facial Gender Classification in Videos 6th

Exploring Local Deep Representations for Facial Gender Class...

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6th international workshop on Artificial Intelligence and pattern recognition (IWAIPR)

作者： Becerra-Riera, Fabiola Morales-Gonzalez, Annette Mendez-Vazquez, Heydi Adv Technol Applicat Ctr 7ma A 21406 Havana Cuba

ISBN: (纸本)9783030011321;9783030011314

Gender recognition in videos is a challenging task that has received limited attention in recent years. To tackle this problem, we propose to explore the use of intermediate features of a Convolutional Neural Network (CNN) with a component-based face representation methodology. With this approach we intend to exploit the gender information provided by different face parts. the features extracted from video key frames are combined with two different strategies to preserve the temporal information, and Random Forest classifiers are employed to obtain a final gender prediction for a video sequence. Our results on the McGill and COX datasets show that our proposal outperforms the end-to-end CNN approach and, in the McGill dataset, 100% of accuracy was obtained.

关键词： Soft-biometrics Gender classification Video face analysis Deep learning representation

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An sEMG-based Human-Robot Interface for Robotic Hands Using Machine Learning and Synergies

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IEEE TRANSACTIONS ON COMPONENTS PACKAGING AND MANUFACTURING TECHNOLOGY 2018年第7期8卷 1149-1158页

作者： Meattini, R. Benatti, S. Scarcia, U. De Gregorio, D. Benini, L. Melchiorri, C. Univ Bologna Dept Elect Elect & Informat Engn I-40126 Bologna Italy Swiss Fed Inst Technol Dept Informat Technol & Elect Engn CH-8092 Zurich Switzerland

Developing natural control strategies represents an intriguing challenge in the design of human-robot interface (HRI) systems. the teleoperation of robotic grasping devices, especially in industrial, rescue, and aerospace applications, is mostly based on nonintuitive approaches, such as remote controllers. On the other hand, recent research efforts target solutions that mimic the human ability to manage multifinger grasps and finely modulate grasp impedance. Since electromyography (EMG) contains information about human motion control, it is possible to leverage such neuromuscular knowledge to teleoperate robotic hands for grasping tasks. In this paper, we present an HRI system based on eight fully differential EMG sensors connected to a wearable sensor node for acquisition and processing. By virtue of a novel bio-inspired approach, the embedded myocontroller merges pattern recognition and factorization techniques to combine a natural selection of the robotic hand configuration with the proportional control of the related grasps. the HRI system has been fully designed, implemented, and tested on two robotic hands: a dexterous anthropomorphic hand and a three-fingered industrial gripper mounted on a robotic manipulator. the results of the test performed on four able-bodied subjects show success rates greater than 90% reached in grasping objects that require different hand shapes and impedance regulations for the task completion. the outcomes also show that the users modulate the bio-inspired degrees of control in a natural manner, proving the pertinence of the proposed system for an effective human-like control of robotic grasping devices in a wearable form factor.

关键词： Electromyography (EMG) sensors embedded systems human-robot interface (HRI) industrial gripper grasping non-negative matrix factorization (NMF) pattern recognition robotic hands

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Partial Discharge pattern recognition with Data Augmentation based on Generative Adversarial Networks 7

Partial Discharge Pattern Recognition with Data Augmentation...

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7th international Conference on Condition Monitoring and Diagnosis, CMD 2018

作者： Wang, Xuhong Huang, Hongyi Hu, Yue Yang, Yupu Ministry of Education Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781538641262

pattern recognition of partial discharge (PD) has various beneficial applications in academia and industry. However, it is hard and expensive to obtain extensively annotated PD data to build a high-performance classification model. Given there is potential to use PD data more effectively, this paper presents a novel data generative model based on generative adversarial networks (GAN). GAN are able to learn deep feature representations of existing PD signals and synthesizes more extensive UHF PD signals. An original PD data set of UHF PD signals is established by the partial discharge experiment. With the result of the original PD signals dataset as baseline, we evaluated the performance of some classifiers on the augmented dataset. the results show that UHF PD classification model benefits from GAN-based data augmentation techniques. Clearly, the GAN-based model has good potential in industry diagnosis of PD, especially when the data are sparse. © 2018 IEEE.

关键词： Generative adversarial networks

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Optimising the the Volgenant-Jonker algorithm for approximating graph edit distance

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pattern recognition LETTERS 2017年 87卷 47-54页

作者： Jones, William Chawdhary, Aziem King, Andy Univ Kent Sch Comp Canterbury CT2 7NF Kent England

Although it is agreed that the Volgenant-Jonker (VJ) algorithm provides a fast way to approximate graph edit distance (GED), until now nobody has reported how the VJ algorithm can be tuned for this task. To this end, we revisit VJ and propose a series of refinements that improve both the speed and memory footprint without sacrificing accuracy in the GED approximation. We quantify the effectiveness of these optimisations by measuring distortion between control-flow graphs: a problem that arises in malware matching, We also document an unexpected behavioural property of VJ ill which the time required to find shortest paths to unassigned vertices decreases as graph size increases, and explain how this phenomenon relates to the birthday paradox. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Attributed graphs graph edit distance Volgenate-Jonker algorithm

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Optimum-Path Forest based on k-connectivity: theory and applications

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pattern recognition LETTERS 2017年第Feb.1期87卷 117-126页

作者： Papa, Joao Paulo Nachif Fernandes, Silas Evandro Falcao, Alexandre Xavier Sao Paulo State Univ Dept Comp Av Eng Luiz Edmundo Carrijo Coube BR-17033360 Bauru SP Brazil Univ Fed Sao Carlos Dept Comp Rod Washington LuisKm 235 BR-13565905 Sao Carlos SP Brazil Univ Estadual Campinas Inst Comp Av Albert Einstein 1251 BR-13083852 Campinas SP Brazil

graph-based pattern recognition techniques have been in the spotlight for many years, since there is a constant need for faster and more effective approaches. Among them, the Optimum-Path Forest (OPF) framework has gained considerable attention in the last years, mainly due to the promising results obtained by OPF-based classifiers, which range from unsupervised, semi-supervised and supervised learning. In this paper, we consider a deeper theoretical explanation concerning the supervised OPF classifier with k-neighborhood (OPFk), as well as we proposed two different training and classification algorithms that allow OPFk to work faster. the experimental validation against standard OPF and Support Vector Machines also validates the robustness of OPFk in real and synthetic datasets. (C) 2016 Elsevier B.V. All rights reserved.

关键词： pattern classification Optimum-Path Forest Supervised learning

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A Survey on Applications of Bipartite graph Edit Distance 11th

A Survey on Applications of Bipartite Graph Edit Distance

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11th IAPR-TC15 international workshop on graph-based representations in pattern recognition (GbRPR)

作者： Stauffer, Michael Tschachtli, thomas Fischer, Andreas Riesen, Kaspar Univ Appl Sci & Arts Northwestern Switzerland Inst Informat Syst Riggenbachstr 16 CH-4600 Olten Switzerland Univ Fribourg Dept Informat CH-1700 Fribourg Switzerland Univ Appl Sci & Arts Western Switzerland Inst Complex Syst CH-1705 Fribourg Switzerland Univ Pretoria Dept Informat Pretoria South Africa

ISBN: (纸本)9783319589619;9783319589602

About ten years ago, a novel graph edit distance framework based on bipartite graph matching has been introduced. this particular framework allows the approximation of graph edit distance in cubic time. this, in turn, makes the concept of graph edit distance also applicable to larger graphs. In the last decade the corresponding paper has been cited more than 360 times. Besides various extensions from the methodological point of view, we also observe a great variety of applications that make use of the bipartite graph matching framework. the present paper aims at giving a first survey on these applications stemming from six different categories (which range from document analysis, over biometrics to malware detection).

关键词： Applications of bipartite graph matching graph-based pattern representations

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On Time-Frequency Image Processing for Change Detection Purposes 7th

On Time-Frequency Image Processing for Change Detection Purp...

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7th international workshop on Soft Computing Applications (SOFA)

作者： Aiordachioaie, Dorel Dunarea de Jos Univ Gala Elect & Telecommun Dept 47 Domneasca St Galati 80008 Romania

ISBN: (纸本)9783319625218

Change detection and diagnosis are important activities in many domains, such as mechanical, aerospace, biomedical and seismic engineering. Process monitoring systems based on vibration measuring and processing use various transforms, for instance time-frequency transforms. Wigner-Ville distribution is used as an example of time-frequency transform, but it generates many interference terms. Some basic properties of the input signal, concerning the number of components, the average time and frequency, the bandwidth and time duration are estimated by computing statistical moments and the Renyi entropy. the paper also presents some results of the evaluation of an automatic system to detect and remove artifacts from time-frequency images. the point is to build a database with blocks, whose size depend on application, with and without interferences (bad blocks), and - by cross-correlation -to detect these blocks in the processed image. In the present state of development, the system is working for some particular structures, concerning the number and the parameters of the analyzed signal components. Adding knowledge about the analyzed signal, the performances can be improved. the results are encouraging and suggest optimization of the proposed method.

关键词： Time-frequency transform Image Detection Signal processing pattern recognition

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