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Graph Reduction Neural Networks for structural pattern recognition

Graph Reduction Neural Networks for Structural Pattern Recog...

joint IAPR International Workshop on structural and Syntactic pattern recognition (SSPR) / Statistical Techniques in pattern recognition (SPR)

作者： Gillioz, Anthony Riesen, Kaspar Univ Bern Inst Comp Sci Bern Switzerland Univ Appl Sci Northwestern Switzerland Inst Informat Syst Olten Switzerland

ISBN: (纸本)9783031230271;9783031230288

In industry, business, and science large amounts of data are produced and collected. In some of these data applications, the underlying entities are inherently complex, making graphs the representation formalism of choice. Actually, graphs endow us with both representational power and flexibility. On the other hand, methods for graph-based data typically have high algorithmic complexity hampering their application in domains that comprise large entities. This motivates to research graph-based algorithms that are both efficient and reliable. In the present paper, we introduce a novel pattern recognition framework that consists of two basic parts. First, we substantially reduce the size of the original graphs by means of a graph neural network. Eventually, we use the reduced graphs in conjunction with graph edit distance and a distance-based classifier. On five datasets we empirically confirm the benefit of the novel reduction scheme regarding both classification performance and computation time.

关键词： structural pattern recognition Graph matching Graph reduction Graph neural network

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Continuous Car Driving Intent Detection Using structural pattern recognition

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2021年第2期22卷 1001-1013页

作者： Lee, Sukhan Khan, Muhammad Qasim Husen, Mohd Nizam Sungkyunkwan Univ SKKU Intelligent Syst Res Inst Dept Elect & Comp Engn Suwon 440746 South Korea Univ Kuala Lumpur Malaysian Inst Informat Technol UniKL MIIT Kuala Lumpur 50250 Malaysia

The early detection of a driver's intention prior to the initiation of actual maneuvering is to offer effective means of assisting the driver in times of safety. Conventional approaches in predicting a driver's intention include framing the motion parameters of the car and driver as well as the eye gaze sequence of the driver into the Hidden Markov Model (HMM) for analysis. However, their power of describing driver behavior is not only limited but also their performance, indicated by the recognition rate relative to the amount of preceding time in early detection, is not satisfactory and needs further improvement. This paper presents an approach for early detection of a driver's intention by modeling and analyzing driver behavior by using structural pattern recognition based on context-free and context-sensitive grammars. We specifically structured a sequence of the driver's eye fixations as well as of the vehicle speed, steering angle and signaling into a sequence of symbolic vectors to form sentences representing a specific driver behavior. It turns out that the proposed approach resulted in an average of 70.5% and 80% recognition rates at the respective 2 and 1 second preceding time to the actual initiation of maneuvering behavior. This performance can be compared to 56.8% and 69% by the state-of-the-art conventional results.

关键词： Vehicles pattern recognition Hidden Markov models Syntactics Accidents Grammar Safety Advanced driver assistance system context-free grammar context-sensitive grammar driver intent eye tracking intelligent transportation system pattern recognition structural pattern recognition syntactic pattern recognition

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Graphs in Computer Vision then and now: how Deep Learning has reinvigorated structural pattern recognition 22

Graphs in Computer Vision then and now: how Deep Learning ha...

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31st ACM Web Conference (WWW)

作者： Conte, Donatello Univ Tours Lab Informat Fondamentale & Appl Tours LIFAT EA 6 Tours France

ISBN: (纸本)9781450391306

Computer Vision Problems, such as object detection, object tracking, action recognition and so on, have been, in the past, usually addressed through Statistical pattern recognition techniques. SVM, Regression or Neural Networks, are some examples of classical statistical techniques that have been used, quite effectively, in many application contexts of computer vision. Nevertheless, some attempts have been proposed using more complex data structures (notably graphs) for solving Computer Vision Tasks. However, in terms of performances, their use did not have the same success as techniques based on vector representations. First part of this talk will present some of these proposals, in the context of object tracking ([1]), people re-identification ([3]) and action recognition ([2]). An graph representation is proposed in [1] to deal with occlusion problem. The representation is based on a graph pyramid, namely, each moving region is represented at different levels of resolution using a graph for each level. The algorithm compares the topmost levels of each pyramid in the association phase between moving objects in two consecutive frames. If the comparison outcome is sufficient to assign a label to each node the tracking algorithm stops. Instead, if some ambiguities arise (as it is the case when two objects over- lap), the algorithm is repeated using the next levels of the pyramids, until either a consistent labelling is found. The purpose of re-identification (reid) is to identify people coming back into the field of view of a camera or to recognize an individual through different cameras in a distributed network. At the heart of the process there is a comparison between signatures given probe and gallery sets. In [3] graphs are used to represent people appearance and comparison is done by means of Graph Kernels. Finally, action recognition is a classification problem in which each video representing an action has to be classified with the correct action label. In [2]

关键词： Computer Vision structural pattern recognition Graph Neural Networks

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Bounding Edit Distance for similarity-based sequence classification on structural pattern recognition

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APPLIED SOFT COMPUTING 2020年第PartA期97卷 106778-106778页

作者： Rico-Juan, Juan R. Valero-Mas, Jose J. Inesta, Jose M. Univ Alicante Dept Software & Comp Syst Carretera San Vicente del Raspeig S-N Alicante 03690 Spain

pattern recognition tasks in the structural domain generally exhibit high accuracy results, but their time efficiency is quite low. Furthermore, this low performance is more pronounced when dealing with instance-based classifiers, since, for each query, the entire corpus must be evaluated to find the closest prototype. In this work we address this efficiency issue for the Nearest Neighbor classifier when data are encoded as two-dimensional code sequences, and more precisely strings and sequences of vectors. For this, a set of bounds is proposed in the distance metric that avoid the calculation of unnecessary distances. Results obtained prove the effectiveness of the proposal as it reduces the classification time in percentages between 80% and 90% for string representations and between 60% and 80% for data codified as sequences of vectors with respect to their corresponding non-optimized version of the classifier. (C) 2020 Elsevier B.V. All rights reserved.

关键词： structural pattern recognition Efficient search Classification Nearest neighbor Edit distance

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The state inspection of upper supporting roller of conveyor in service with structural pattern recognition and cluster norm algorithm of failure

The state inspection of upper supporting roller of conveyor ...

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作者： Ehui Huang Chunyang Gu Shanping Gao Jinling Song Shuting Li Junqin Zhou Zhenjie Chen Lizhi Gu Key Laboratory of Virtual Manufacturing Technology of Fujian Universities Quanzhou University of Information Engineering China University of Nottingham Quanzhou Normal University College of Mechanical Engineering and Automation Huaqiao University

In thermal power generation,coal transportation is an important link in the production process,and coal transportation by corridor conveyor is a common technical *** order to ensure the normal and continuous supply of coal,it is necessary to inspect the corridor facilities,especially the working status of conveyor rollers,and timely find and deal with abnormal *** is difficult to meet the requirements of modern thermal coal transportation inspection due to the heavy workload and high labor intensity.A kind of inspection robot instead of manual is proposed and *** robot system by CCD detection,convention scanning way,classifier design and abnormal structure pattern recognition algorithm,the displacement of conveyor roller state,center,loss of revolution,abstraction of severe abrasion and crack,forms the state vector,through clustering algorithm of norm,judgment is done for determination of the roller on the working state.

关键词： State vector structural pattern recognition Machine vision Conveyor upper roller state Cluster norm algorithm

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NLP-inspired structural pattern recognition in chemical application

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pattern recognition LETTERS 2014年第1期45卷 11-16页

作者： Sidorova, J. Anisimova, M. Swiss Fed Inst Technol Swiss Fed Inst Technol Dept Comp Sci Computat Biochem Res Grp Zurich Switzerland Swiss Inst Bioinformat Zurich Switzerland

In this paper we report on a new structural pattern recognition approach for in silico prediction of chemical activity. It is based on grammatical inference on strings representing chemical compounds and string edit distance between a chemical compound and a formal grammar generalizing an activity class. In the late 1980s Weininger published a chemical language with a very simple and natural grammar. Recently, the algorithms suitable to process this language have been developed. From modeling of chemical activity with formal grammars and chemical compounds as words, a functionality is derivable to search for "structural alerts", that is, molecular substructures and their combinatorial patterns that cause a molecule to have properties of interest. A biodegradability prediction system has been constructed to serve as an example throughout the paper. The source code and various files from the experiment are available from the corresponding author on request. (C) 2014 Elsevier B.V. All rights reserved.

关键词： structural pattern recognition Grammar inference Natural language processing Chemical descriptors SMILES Activity prediction

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Stochastic reasoning for structural pattern recognition: An example from image-based UAV navigation

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pattern recognition 2014年第8期47卷 2732-2744页

作者： Michaelsen, Eckart Meidow, Jochen Fraunhofer IOSB D-76275 Ettlingen Germany

This paper reports on the statistical embedding of a structural pattern recognition system into the autonomous navigation of an unmanned aerial vehicle (UAV). A rule-based system is used for the recognition of visual landmarks such as bridges in aerial views. In principle, rule-based systems can be designed and coded with no training data at hand, but a sound interpretation and utilization of the achieved results needs statistical inference and representative data sets of sufficient coverages. Flying a UAV with an experimental system is expensive, risky, and legally questionable. Therefore, we chose a virtual globe as a camera simulator providing arbitrary amounts of training and test data. The expected positions of landmarks in the aerial views are modeled by mixture models representing inliers, outliers, and intermediate forms which stem from similar structures appearing frequently in the vicinity of landmarks. The parameters of the corresponding likelihood functions are estimated by the Expectation-Maximization method. Using these estimates, we carry out tests and compare the results for heuristic, pessimistic, optimistic, and Bayesian decision rationales. This performance evaluation reveals the superiority of the Bayesian approach. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： structural pattern recognition Decision rationales Expectation-Maximization Navigation UAV

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Merged LSTM-based pattern recognition of structural behavior of cable-supported bridges

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第1期125卷

作者： Min, Seongi Lee, Yunwoo Byun, Yong-Hoon Kang, Young Jong Kim, Seungjun Korea Univ Sch Civil Environm & Architectural Engn Seoul 02841 South Korea Chungbuk Natl Univ Sch Civil Engn Cheongju 28644 South Korea Kyungpook Natl Univ Sch Agr Civil & Bioind Engn Daegu 41566 South Korea

structural responses of bridges occur based on their structural characteristics and conditions. After the structural pattern is identified from the long-term measured response datasets, the structural responses can be evaluated and predicted using a pattern model. In the absence of significant variations in the structural condition, the difference between the predicted and measured responses is negligible. Otherwise, the differences can be identified, and this would be evidence of the variation in the structural condition. Therefore, the structural pattern model can be used effectively to investigate variations in the structural state and conditions. This study proposes an effective structural pattern recognition method using deep learning. A merged model is proposed by combining deep neural network (DNN) and long short-term memory (LSTM) algorithms to handle long-term responses from various sensors in the time domain and reflect statistical properties. Long-term (five-year) measured response datasets of an existing cable-supported bridge were used to validate the proposed method. According to the study, the proposed method can effectively identify the structural behavioral pattern of a cable-supported bridge.

关键词： Cable -supported bridge structural pattern recognition structural health monitoring Deep learning Long short-term memory Long-term measured data

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Evaluating structural pattern recognition for Handwritten Math via Primitive Label Graphs

Evaluating Structural Pattern Recognition for Handwritten Ma...

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20th Conference on Document recognition and Retrieval (DRR) held as part of the IS and T/SPIE Symposium on Electronic Imaging

作者： Zanibbi, Richard Mouchere, Harold Viard-Gaudin, Christian Rochester Inst Technol Dept Comp Sci 1 Lomb Mem Dr Rochester NY 14623 USA Univ Nantes LUNAM IRCCyN F-44306 Nantes France

ISBN: (纸本)9780819494313

Currently, structural pattern recognizer evaluations compare graphs of detected structure to target structures (i.e. ground truth) using recognition rates, recall and precision for object segmentation, classification and relationships. In document recognition, these target objects (e. g. symbols) are frequently comprised of multiple primitives (e. g. connected components, or strokes for online handwritten data), but current metrics do not characterize errors at the primitive level, from which object-level structure is obtained. Primitive label graphs are directed graphs defined over primitives and primitive pairs. We define new metrics obtained by Hamming distances over label graphs, which allow classification, segmentation and parsing errors to be characterized separately, or using a single measure. Recall and precision for detected objects may also be computed directly from label graphs. We illustrate the new metrics by comparing a new primitive-level evaluation to the symbol-level evaluation performed for the CROHME 2012 handwritten math recognition competition. A Python-based set of utilities for evaluating, visualizing and translating label graphs is publicly available.

关键词： Evaluation Performance Metrics structural pattern recognition Math recognition Graphics recognition

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Learning Graph Matching with Graph Neural Networks 11th

Learning Graph Matching with Graph Neural Networks

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11th IAPR TC3 Workshop on Artificial Neural Networks in pattern recognition (ANNPR)

作者： Dobler, Kalvin Riesen, Kaspar Univ Bern Inst Comp Sci Neubruckstr 10 CH-3012 Bern Switzerland

ISBN: (纸本)9783031716010;9783031716027

Graph matching aims at evaluating the dissimilarity of two graphs by defining a constrained correspondence between their nodes and edges. Error-tolerant graph matching, for instance, introduces the concept of a cost for penalizing structural differences in the matching. A popular method for this approach is graph edit distance, which is based on the cost of the minimal sequence of edit operations to transform a source graph into a target graph. One of the main problems of graph edit distance is the computational complexity, which is exponential in its exact form. In recent years, several approximation methods for graph edit distance have been presented which offer polynomial runtimes. In this paper, we approach the graph edit distance problem in a fundamentally different way. In particular, we propose to learn graph edit distance by means of graph neural networks. In a comprehensive experimental evaluation on six data sets, we verify that our approach not only provides comparable classification performance but also substantially reduces the runtime compared to a prominent algorithm for approximate graph edit distance computation.

关键词： structural pattern recognition Graph Matching Graph Edit Distance Graph Representation Learning

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