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4th international conference on Analysis of Images, Social Networks and Texts, AIST 2015

4th International Conference on Analysis of Images, Social N...

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4th international conference on Analysis of Images, Social Networks and Texts, AIST 2015

ISBN: (纸本)9783319261225

the proceedings contain 42 papers. the special focus in this conference is on Analysis of Images, Videos, pattern recognition, machine learning and Social Network Analysis. the topics include: A probabilistic rating system for team competitions with individual contributions;sequential hierarchical image recognition based on the pyramid histograms of oriented gradients with small samples;normalization of non-standard words with finite state transducers for Russian speech synthesis;transform coding method for hyperspectral data;fast global image denoising algorithm on the basis of nonstationary gamma-normal statistical model;theoretical approach to developing efficient algorithms of fingerprint enhancement;remote sensing data verification using model-oriented descriptors;frequency analysis of gradient descent method and accuracy of iterative image restoration;shape matching based on skeletonization and alignment of primitive chains;color image restoration with fuzzy Gaussian mixture model driven nonlocal filter;a phase unwrapping algorithm for interferometric phase images;robust image watermarking on triangle grid of feature points;traffic flow forecasting algorithm based on combination of adaptive elementary predictors;analysis of the adaptive nature of collaborative filtering techniques in dynamic environment;a texture fuzzy classifier based on the training set clustering by a self-organizing neural network;distorted high-dimensional binary patterns search by scalar neural network tree;hybrid classification approach to decision support for endoscopy in gastrointestinal tract;user similarity computation for collaborative filtering using dynamic implicit trust and distributed coordinate descent for l1-regularized logistic regression.

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Towards pose-free tracking of non-rigid face using synthetic data 4

Towards pose-free tracking of non-rigid face using synthetic...

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4th international conference on pattern recognition Applications and Methods, ICPRAM 2015

作者： Tran, Ngoc-Trung Ababsa, Fakhreddine Charbit, Maurice LTCI Telecom ParisTech 37-39 rue Dareau Paris75014 France IBISC University of Evry 40 rue du Pelvoux Evry91020 France

ISBN: (纸本)9789897580772

the non-rigid face tracking has been achieved many advances in recent years, but most of empirical experiments are restricted at near-frontal face. this report introduces a robust framework for pose-free tracking of non-rigid face. Our method consists of two phases: training and tracking. In the training phase, a large offline synthesized database is built to train landmark appearance models using linear Support Vector machine (SVM). In the tracking phase, a two-step approach is proposed: the first step, namely initialization, benefits 2D SIFT matching between the current frame and a set of adaptive keyframes to estimate the rigid parameters. the second step obtains the whole set of parameters (rigid and non-rigid) using a heuristic method via pose-wise SVMs. the combination of these aspects makes our method work robustly up to 90° of vertical axial rotation. Moreover, our method appears to be robust even in the presence of fast movements and tracking losses. Comparing to other published algorithms, our method offers a very good compromise of rigid and non-rigid parameter accuracies. this study gives a promising perspective because of the good results in terms of pose estimation (average error is less than 4° on BUFT dataset) and landmark tracking precision (5.8 pixel error compared to 6.8 of one state-of-the-art method on Talking Face video). these results highlight the potential of using synthetic data to track non-rigid face in unconstrained poses.

关键词： Heuristic methods

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4th international conference on Soft Computing, Intelligent Systems and Information Technology, ICSIIT 2015

4th International Conference on Soft Computing, Intelligent ...

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4th international conference on Soft Computing, Intelligent Systems and Information Technology, ICSIIT 2015

ISBN: (纸本)9783662467411

the proceedings contain 68 papers. the special focus in this conference is on Fuzzy logic and control system, Genetic algorithm and heuristic approaches, Artificial intelligence and machine learning, Similarity-based models, Classification and clustering techniques, Intelligent data processing, Feature extraction and Image recognition. the topics include: A study of laundry tidiness: laundry state determination using video and 3D sensors;direction control system on a carrier robot using fuzzy logic controller;multidimensional fuzzy association rules for developing decision support system at petra christian university;genetic algorithm for scheduling courses;optimization of auto equip function in role-playing game based on standard deviation of character’s stats using genetic algorithm;the design of net energy balance optimization model for crude palm oil production;ACO-LS algorithm for solving no-wait flow shop scheduling problem;implementation discrete cosine transform and radial basis function neural network in facial image recognition;implementation of artificial intelligence with 3 different characters of AI player on “Monopoly Deal” computer game;optimizing instruction for learning computer programming - a novel approach;the comparation of distance-based similarity measure to detection of plagiarism in indonesian text;document searching engine using term similarity vector space model on english and indonesian document;knowledge representation for image feature extraction;the data analysis of stock market using a frequency integrated spherical hidden markov self organizing map and a search engine development utilizing unsupervised learning approach.

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Automatic wedge tightness classifying system by support vector machine 6

Automatic wedge tightness classifying system by support vect...

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2015 6th international conference on Information and Communication Technology for Embedded Systems (IC-ICTES)

作者： Poombansao, thanachai Kongprawechnon, Waree theeraworn, Chonlada Kittipiyakul, Somsak Thammasat Univ Sirindhorn Int Inst Technol Sch Informat Comp & Commun Technol Pathum Thani Thailand NECTEC Pathum Thani Thailand

ISBN: (纸本)9781479985647

this paper introduces a newly developed automatic classification system for wedge tightness inside the generator by applying support vector machine (SVM) classifier. the automatic classifying system for wedge tightness of the generator consists of 4 parts including data collection, preprocessing, feature extraction, and classification. machine learning algorithm called SVM is used with the linear and radial basis function (RBF) classifier. Each input feature is extracted in different ways to evaluate the performance of classification. the evaluation is completed by using a 10- fold cross validation technique to provide high accuracy and a low number of False Negatives (FN). By applying the proposed system, the number of tightness and looseness inside wedge generator can be classified. Based on the classification results, the signals extracted in the frequency domain gives the best performance among the time domain and the frequency domain. this paper shows that the automatic classifying method has a high potential to identify the wedge tightness inside the generator.

关键词： wedge tightness signal pattern recognition support vector machine

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HEp-2 staining pattern recognition using stacked fisher network for encoding Weber local descriptor 1

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6th international Workshop on machine learning in Medical Imaging, MLMI 2015 and Held in Conjunction with 18th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2015

作者： Han, Xian-Hua Chen, Yen-Wei Xu, Gang Ritsumeikan University 1-1-1 NojiHigashi Kusatsu Shiga525-8577 Japan

ISBN: (数字)9783319248882

ISBN: (纸本)9783319248875

this study addresses the recognition problem of the HEp-2 cell using indirect immunofluorescent (IIF) image analysis, which can indicate the presence of autoimmune diseases by finding antibodies in the patient serum. Generally, the method used for IIF analysis remains subjective, and depends too heavily on the experience and expertise of the physician. this study aims to explore an automatic HEp-2 cell recognition system, in which how to extract highly discriminate visual features plays a key role in this recognition application. In order to realize this purpose, our main efforts include: (1) a transformed excitation domain instead of the raw image domain, which is based on the fact that human perception for disguising a pattern depends not only on the absolute intensity of the stimulus but also on the relative variance of the stimulus;(2) a simple but robust micro-texton without any quantization in the excitation domain, called as Weber local descriptor (WLD);(3) a data-driven coding strategy with a parametric probability process, and the extraction of not only low- but also high-order statistics for image representation called as Fisher vector;(4) the stacking of the Fisher network into deep learning framework for more discriminate feature. Experiments using the open HEp-2 cell dataset released in the ICIP2013 contest validate that the proposed strategy can achieve a much better performance than the state-of-the-art approaches, and that the achieved recognition error rate is even very significantly below the observed intra-laboratory variability. © Springer international Publishing Switzerland 2015.

关键词： Deep learning

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Deep learning for Feature Extraction of Arabic Handwritten Script 16th

Deep Learning for Feature Extraction of Arabic Handwritten S...

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16th international conference on Computer Analysis of Images and patterns (CAIP)

作者： Elleuch, Mohamed Tagougui, Najiba Kherallah, Monji Univ Manouba Natl Sch Comp Sci ENSI Manouba Tunisia Univ Sfax ATMS Sfax Tunisia Univ Gabes Higher Inst Management Gabes Gabes Tunisia

ISBN: (纸本)9783319231174;9783319231167

In recent years, systems based on deep learning have gained great popularity in the pattern recognition filed. this is basically to benefit from the hierarchical representations used to unlabeled data which is becoming the focus of many researchers since it represents the easiest way to deal with a huge amount of data. Most of the architecture in deep learning is constructed by a stack of feature extractors, such as Restricted Boltzmann machine and Auto-Encoder. In this paper, we highlight how these deep learning techniques can be effectively applied for recognizing Arabic Handwritten Script (AHS) and this by investigating two deep architectures: Deep Belief Networks (DBN) and Convolutional Deep Belief Networks (CDBN) which are applied respectively on low-level dimension and high-level dimension in textual images. the experimental study has proved promising results which are comparable to the state-of-the-art Arabic OCR.

关键词： Deep learning Feature extractors CDBN Arabic Handwritten Script

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Identifying and mitigating labelling errors in active learning 4th

Identifying and mitigating labelling errors in active learni...

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4th international conference on pattern recognition Applications and Methods, ICPRAM 2015

作者： Bouguelia, Mohamed-Rafik Belaïd, Yolande Belaïd, Abdel Université de Lorraine - LORIA UMR 7503 Vandoeuvre-les-Nancy54506 France

ISBN: (纸本)9783319276762

Most existing active learning methods for classification, assume that the observed labels (i.e. given by a human labeller) are perfectly correct. However, in real world applications, the labeller is usually subject to labelling errors that reduce the classification accuracy of the learned model. In this paper, we address this issue for active learning in the streaming setting and we try to answer the following questions: (1) which labelled instances are most likely to be mislabelled? (2) is it always good to abstain from learning when data is suspected to be mislabelled? (3) which mislabelled instances require relabelling? We propose a hybrid active learning strategy based on two measures. the first measure allows to filter the potentially mislabelled instances, based on the degree of disagreement among the manually given label and the predicted class label. the second measure allows to select (for relabelling) only the most informative instances that deserve to be corrected. An instance is worth relabelling if it shows highly conflicting information among the predicted and the queried labels. Experiments on several real world data show that filtering mislabelled instances according to the first measure and relabelling few instances selected according to the second measure, greatly improves the classification accuracy of the stream-based active learning. © Springer international Publishing Switzerland 2015.

关键词： learning systems

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Applying Clustering Analysis to Heterogeneous data Using Similarity Matrix Fusion (SMF) 11th

Applying Clustering Analysis to Heterogeneous Data Using Sim...

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11th international conference on machine learning and data Mining (MLDM)

作者： Mojahed, Aalaa Bettencourt-Silva, Joao H. Wang, Wenjia de la Iglesia, Beatriz Univ E Anglia Norwich NR4 7TJ Norfolk England

ISBN: (纸本)9783319210247;9783319210230

We define a heterogeneous dataset as a set of complex objects, that is, those defined by several data types including structured data, images, free text or time series. We envisage this could be extensible to other data types. there are currently research gaps in how to deal with such complex data. In our previous work, we have proposed an intermediary fusion approach called SMF which produces a pairwise matrix of distances between heterogeneous objects by fusing the distances between the individual data types. More precisely, SMF aggregates partial distances that we compute separately from each data type, taking into consideration uncertainty. Consequently, a single fused distance matrix is produced that can be used to produce a clustering using a standard clustering algorithm. In this paper we extend the practical work by evaluating SMF using the k-means algorithm to cluster heterogeneous data. We used a dataset of prostate cancer patients where objects are described by two basic data types, namely: structured and time-series data. We assess the results of clustering using external validation on multiple possible classifications of our patients. the result shows that the SMF approach can improved the clustering configuration when compared with clustering on an individual data type.

关键词： Heterogeneous data Big data Distance measure Intermediate data fusion Clustering Uncertainty

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4th international conference on Frontiers in Intelligent Computing: theory and Applications, FICTA 2015

4th International Conference on Frontiers in Intelligent Com...

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4th international conference on Frontiers in Intelligent Computing: theory and Applications, FICTA 2015

ISBN: (纸本)9788132226932

the proceedings contain 61 papers. the special focus in this conference is on Advances in Nature Inspired Algorithms for Engineering Optimization Problems, Bioinformatics and Computational Biology. the topics include: An efficient dynamic scheduling algorithm for soft real-time tasks in multiprocessor system using hybrid quantum-inspired genetic algorithm;equitable machine learning algorithms to probe over P2P botnets;directed search-based PSO algorithm and its application to scheduling independent task in multiprocessor environment;prediction of phosphorylation sites using a consensus of MLP classifiers;SVM-based pre-microrna classifier using sequence, structural, and thermodynamic parameters;a novel differentially coexpressed gene module detection algorithm using spectral clustering;intelligent topological differential gene networks;impact of threshold to identify vocal tract;application of compressed sensing in cognitive radio;compressed sensing-based NBI mitigation in ultra-wideband energy detector;a novel data mining scheme for smartphone activity recognition by accelerometer sensor;mining and ranking association rules in support, confidence, correlation, and dissociation framework;mining closed interesting subspaces to discover conducive living environment of migratory animals;frequent patterns mining from data cube using aggregation and directed graph;a modified parallel thinning method for handwritten oriya character images;offline signature verification using artificial neural network;a simple and effective technique for online handwritten bangla character recognition;offline writer identification from isolated characters using textural features and application of krill herd algorithm for optimum design of load frequency controller for multi-area power system network with generation rate constraint.

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the application of autoencoder in classification of the eye movement data 4

The application of autoencoder in classification of the eye ...

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4th international conference on Information Science and Cloud Computing, ISCC 2015

作者： Zhang, Mengjie Li, Mi Sun, Jiankang Lu, Shengfu Zhong, Ning International WIC Institute Beijing University of Technology Beijing100124 China Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing100124 China Beijing Key Laboratory of MRI and Brain Informatics Beijing100053 China Maebashi Institute of Technology Maebashi-City371-0816 Japan

Deep learning well demonstrates its potential in learning latent feature representations, and has been applied in the fields of speech recognition, image identification and information retrieval. Deep learning architecture is composed of multilayer non-linear units, each low layer's output as a input of higher layer, and can learn high-order feature representations which contain many structural information from a large number of data. Deep learning is a good way to extract features from original data. Web page is an important human-machine interface. Identify users' visual behavior on Web pages will promote human-machine interaction. this paper apply stacked auto-encoders (SAE) with logistic regression to build classification model. this model effectively solves the problem of identifying users' working state of visual search and visual browse on Web pages. the experiment shows that this model outperforms other Single model such as SVM and logistic regression and achieves the accuracy of 90.32%. Further, we embed adaboost algorithm to improve recognition accuracy and precision. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 international License (CC BY-NC-ND 4.0).

关键词： Websites

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