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8th iapr international conference on pattern recognition in bioinformatics, PRIB 2013

ISBN: (纸本)9783642391583

the proceedings contain 25 papers. the topics discussed include: a fast agglomerative community detection method for protein complex discovery in protein interaction networks;inferring gene regulatory networks from time-series expressions using random forests ensemble;local topological signatures for network-based prediction of biological function;mutational genomics for cancer pathway discovery;outlier gene set analysis combined with top scoring pair provides robust biomarkers of pathway activity;restricted neighborhood search clustering revisited: an evolutionary computation perspective;versatile sparse matrix factorization and its applications in high-dimensional biological data analysis;combining protein fragment feature-based resampling and local optimization;experimental determination of intrinsic drosophila embryo coordinates by evolutionary computation;and identifying informative genes for prediction of breast cancer subtypes.

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Person attributes extraction in profiles based on SVM and pattern

Person attributes extraction in profiles based on SVM and pa...

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IEEE international conference on Software Engineering and Service Sciences (ICSESS)

作者： Zhen Zhu Yuan Sun School of Information Engineering Minzu University of China Beijing China

ISBN: (纸本)9781479983544

this paper is an exploration to find a way to get the person attributes in profiles. Considering those attributes exists in large volume of unstructured data, and it is very difficult to gain in a short time. So, we use a method combing the pattern and SVM to extract the person attributes. Firstly, we collect many raw profiles in websites by our configurable crawler. Secondly, we use statistic methods to do pre-processing works include lexical analysis and name recognition. thirdly, we build the patterns, which can use in model to extract the person attributes. Also we generalize those patterns to SVM features. Finally, we use SVM assisted with pattern-based method to predict the person attributes. the results prove the method is effective and the data we extracted is useful in building specific-areas' expert database and information retrieval.

关键词： Feature extraction Support vector machines Data mining Kernel Databases Education Employment

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A convolutional neural network combined with aggregate channel feature for face detection

A convolutional neural network combined with aggregate chann...

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6th international conference on Wireless, Mobile and Multi-Media (ICWMMN 2015)

作者： Shuo Wang Bin Yang Zhen Lei Jun Wan Stan Z. Li School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Center for Biometrics and Security Research & National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China China Research and Development Center for Internet of Thing China

ISBN: (纸本)9781510822030

Face detection has been studied intensively over the past several decades and achieved great improvements via convolutional neural network (CNN) which has greatly improved the performance in image classification and object detection. In this paper, similar to the idea of R-CNN [1], we present a new method that combines the aggregate channel features (ACF) [2] and CNN for face detection. the proposed method uses ACF to select the possible human face regions and then trains a CNN model to filter out non-face candidates. then we merge the results of ACF and CNN to get the final detection window(s). Evaluations on two popular face detection benchmark datasets show that our method outperforms the ACF method and has achieved competitive performance against the state-of-the-art algorithms.

关键词： CNN ACF Face detection Facial recognition CLEFT PALATE, CARDIAC DEFECT, GENITAL ANOMALIES, AND ECTRODACTYLY aggregate channel Neural network Image classification Object detection aggregates

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Soccer videos analysis based on the active discriminant model

Soccer videos analysis based on the active discriminant mode...

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IEEE international conference on Software Engineering and Service Sciences (ICSESS)

作者： Cuijuan Zhou Xilong Wang Jian Li Changji Wen College of Information and Technology Jilin Agricultural University Changchun Jilin China

ISBN: (纸本)9781479983544

Since the beginning of the 21th century, with the continuous development of information technology, it has been the difficulty and hotpot in understanding field to study and analyze sports videos with computer technology. Because soccer videos have a broad mass background, it is more valuable to research on it. At present, there are many researches based on trajectory information of players or balls. But due to the background interference and other reasons, the detection accuracy still needs to be improved. In this paper, we present a method of tactical behavior recognition based on the local spatio-temporal regression kernel according to the activity of the players who execute tactics on the playfield. Firstly, we detected the playfield and divided it into some parts. Secondly, we established an active discrimination model by using the local spatio-temporal regression kernel as a future detector for the detection and location of active players, and constructing future bag models. Finally, we divided the tactical behaviors into six classes to realize automatic recognition of them. the method of this paper is used to the video segments of the associated goal events of the 2012 European Cup and the 2013/14 season of Barcelona in the La Liga, and the average accuracy rate of the experiments is 91.3%.

关键词： Videos Feature extraction Detectors pattern recognition Accuracy Europe Visualization

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A novel gender classification method based on MapReduce

A novel gender classification method based on MapReduce

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IEEE international conference on Software Engineering and Service Sciences (ICSESS)

作者： Tong Cui Haifeng Zhao Science and Technology on Information Systems Engineering Laboratory Nanjing China

ISBN: (纸本)9781479983544

A novel parallelize gender recognition method with MapReduce is presented, which successfully comprise several machine leaning algorithms which are employed for gender recognition. the mass of face sample images are gathered and separated as train dataset and test dataset, and Local Binary pattern (LBP) features are extracted when those sample sets are pre-processed and made ready for following operations. And Principle Component Analysis (PCA) is applied to train dataset to extract the most distinguishing features. three classification algorithms: Support Vector Machine(SVM), k-Nearest Neighborhood (k-NN) and Adaboost are implemented and compared to determine the most suitable and successful algorithm for gender parallelize machine learning (GPML). To achieve the shortest execution time, we propose to apply GPML with MapReduce to avoid parallelizing above three algorithms while also improving their scalability to big datasets. the results show that this method reduces the training computational complexity significantly when the number of computing nodes increases while gaining better speedup rates and extending performance than those on parallelize Adaboost.

关键词： face recognition feature extraction image classification learning (artificial intelligence) parallel processing principal component analysis support vector machines Adaboost GPML LBP features MapReduce PCA SVM classification algorithm face sample images gender classification method gender parallelize machine learning k-nearest neighborhood local binary pattern feature extraction machine leaning algorithms parallelized gender recognition method principle component analysis support vector machine test dataset train dataset training computational complexity gender recognition parallel meta-learning Support Vector Network principal components analysis Sorting algorithm Image classification PARALLEL PROCESSING (COMPUTERS) Facial recognition Passive Cutaneous Anaphylaxis Feature extraction Dataset Gender ADABOOST execution time Classification Methods

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6th iapr TC3 international Workshop on Artificial Neural Networks for pattern recognition, ANNPR 2014

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6th iapr TC3 international Workshop on Artificial Neural Networks for pattern recognition, ANNPR 2014

ISBN: (纸本)9783319116556

the proceedings contain 27 papers. the special focus in this conference is on Artificial Neural Networks for pattern recognition. the topics include: Large margin distribution learning;a decorrelation approach for pruning of multilayer perceptron networks;unsupervised active learning of CRF model for cross-lingual named entity recognition;incremental feature selection by block addition and block deletion using least squares SVRs;low-dimensional data representation in data analysis;analyzing dynamic ensemble selection techniques using dissimilarity analysis;hidden Markov models based on generalized Dirichlet mixtures for proportional data modeling;majority-class aware support vector domain oversampling for imbalanced classification problems;forward and backward forecasting ensembles for the estimation of time series missing data;dynamic weighted fusion of adaptive classifier ensembles based on changing data streams;combining bipartite graph matching and beam search for graph edit distance approximation;computing upper and lower bounds of graph edit distance in cubic time;a new multi-class fuzzy support vector machine algorithm;a reinforcement learning algorithm to train a Tetris playing agent;automatic bridge crack detection - a texture analysis-based approach;part-based high accuracy recognition of serial numbers in bank notes;comparative study of feature selection for white blood cell differential counts in low resolution images;end-shape recognition for Arabic handwritten text segmentation;intelligent ensemble systems for modeling NASDAQ microstructure;face recognition based on discriminative dictionary with multilevel feature fusion;investigating of preprocessing techniques and novel features in recognition of handwritten Arabic characters and a time series classification approach for motion analysis using ensembles in ubiquitous healthcare systems.

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A convolutional neural network combined with aggregate channel feature for face detection 6

A convolutional neural network combined with aggregate chann...

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6th international conference on Wireless, Mobile and Multi-Media, ICWMMN 2015

作者： Wang, Shuo Yang, Bin Lei, Zhen Wan, Jun Li, Stan Z. School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China China Research and Development Center for Internet of Thing China Center for Biometrics and Security Research National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781849199940

关键词： Aggregates

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A new multi-class fuzzy support vector machine algorithm 6

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6th iapr TC3 international Workshop on Artificial Neural Networks for pattern recognition, ANNPR 2014

作者： Schwenker, Friedhelm Frey, Markus Glodek, Michael Kächele, Markus Meudt, Sascha Schels, Martin Schmidt, Miriam Ulm University Institute of Neural Information Processing Ulm89069 Germany

ISBN: (纸本)9783319116556

In this paper a novel approach to fuzzy support vector machines (SVM) in multi-class classification problems is presented. the proposed algorithm has the property to benefit from fuzzy labeled data in the training phase and can determine fuzzy memberships for input data. the algorithm can be considered as an extension of the traditional multi-class SVM for crisp labeled data, and it also extents the fuzzy SVM approach for fuzzy labeled training data in the two-class classification setting. Its behavior is demonstrated on three benchmark data sets, the achieved results motivate the inclusion of fuzzy labeled data into the training set for various tasks in pattern recognition and machine learning, such as the design of aggregation rules in multiple classifier systems, or in partially supervised learning. © Springer international Publishing Switzerland 2014.

关键词： pattern recognition

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Face recognition based on discriminative dictionary with multilevel feature fusion 6

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6th iapr TC3 international Workshop on Artificial Neural Networks for pattern recognition, ANNPR 2014

作者： Li, Hongjun Nobile, Nicola Suen, Ching Y. School of Electronic Information Engineering Nantong University Nantong226019 China Centre for Pattern Recognition and Machine Intelligence Concordia University MontrealQCH3G 1M8 Canada

ISBN: (纸本)9783319116556

In order to alleviate the influence of illumination, pose, expression and occlusion variations in face recognition, in this paper, an effective face recognition method based on discriminative sparse representation is proposed. To solve the problem of these variations, we extract discriminative features which represent for each of the training images, and propose a novel dictionary by learning discriminative features. Firstly, we decompose a test image by using nonsubsampled contourlet transform (NSCT), and then fuse the information according to the features from each subband and their contributions. Finally, we obtain the discriminative features of training images and construct a discriminative dictionary. Fuse these multiple features can improve the efficiency and effectiveness of face recognition, especially when training samples are limited and the dimension of feature vector is low. Experimental results on two widely used face databases are presented to demonstrate the efficiency of the proposed approach. © Springer international Publishing Switzerland 2014.

关键词： Face recognition

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A geometrical approach to rejecting option in pattern recognition problem

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in bioinformatics) 2015年 9339卷 231-243页

作者： Ciecierski, Jakub Dybisz, Bartomiej Jastrzebska, Agnieszka Pedrycz, Witold Warsaw University of Technology ul. Koszykowa 75 Warsaw00-662 Poland Systems Research Institute Polish Academy of Sciences ul. Newelska 6 Warsaw01-447 Poland Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2G7 Canada

ISBN: (纸本)9783319243689

Frequently it happens that during symbols recognition, not all of them are the proper ones. this may cause deterioration of a classifying process. In this paper we present a way to "separate the wheat from the chaff", by constructing a rejector, based on geometrical figures enclosing "wheat" and excluding "chaff". We assume that entities of wheat, called native elements, are structured in some way and that there is no a priori knowledge about chaff, named foreign symbols. For the purpose of this study we present simple geometrical figures to generalize the distribution of symbols and to govern the rejection process. © IFIP international Federation for Information Processing 2015.

关键词： Geometry

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