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共 135 条记录，以下是41-50 订阅

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A SIFT-Based Feature Level Fusion of Iris and Ear Biometrics 3

引用

3rd iapr TC3 workshop on pattern recognition of Social Signals in Human-Computer-Interaction (MPRSS)

作者： Ghoualmi, Lamis Chikhi, Salim Draa, Amer Constantine 2 Univ MISC Lab Constantine 25000 Algeria

ISBN: (纸本)9783319148991;9783319148984

To overcome the drawbacks encountoured in unimodal biometric systems for person authentification, multimodal biometrics methods are needed. This paper presents an efficient feature level fusion of iris and ear images using SIFT descriptors which extract the iris and ear features separetely. Then, these features are incorporated in a single feature vector called fused template. The generated template is enrolled in the database, then the matching of SIFT features of iris and ear input images and the enrolled template of the claiming user is computed using Euclidean distance. The proposed method has been applied on a synthetic multimodal biometrics database. The latter is produced from Casia and USTB 2 databases wich represent iris and ear image sets respectively. As the performance evaluation of the proposed method we compute the false rejection rate (FRR), the false acceptance rate (FAR) and accuracy measures. From the obtained results, we can say that the fusion at feature level outperforms iris and ear authentification systems taken separately.

关键词： Multimodal biometrics Feature level fusion SIFT Iris biometrics Ear biometrics

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Prediction of insertion-site preferences of transposons using support vector machines and artificial neural networks 6

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6th iapr TC3 International workshop on artificial neural networks for pattern recognition, ANNPR 2014

作者： Ayat, Maryam Domaratzki, Michael Bioinformatics Lab Department of Computer Science University of Manitoba WinnipegMB R3T 2N2 Canada

ISBN: (纸本)9783319116556

Transposons are segments of DNA that are capable of moving from one location to another within the genome of a cell. Understanding transposon insertion-site preferences is critically important in functional genomics and gene therapy studies. It has been found that the deformability property of the local DNA structure of the integration sites, called Vstep, is of significant importance in the target-site selection process. We considered the Vstep profiles of insertion sites and developed predictors based on artificial neural networks (ANN) and Support Vector Machines (SVM), and trained them with a Sleeping Beauty transposon dataset. We found that both ANN and SVM predictors are excellent in finding the most preferred regions. However, the SVM predictor outperforms the ANN predictor in recognizing preferred sites, in general. © Springer International Publishing Switzerland 2014.

关键词： neural networks

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Semi-Supervised Learning for RGB-D Object recognition 22

Semi-Supervised Learning for RGB-D Object Recognition

引用

22nd International Conference on pattern recognition (ICPR)

作者： Cheng, Yanhua Zhao, Xin Huang, Kaiqi Tan, Tieniu Chinese Acad Sci Natl Lab Pattern Recognit Ctr Res Intelligent Percept & Comp Beijing 100864 Peoples R China

ISBN: (纸本)9781479952083

Conventional supervised object recognition methods have been investigated for many years. Despite their successes, there are still two suffering limitations: (1) various information of an object is represented by artificial features only derived from RGB images;(2) lots of manually labeled data is required by supervised learning. To address those limitations, we propose a new semi-supervised learning framework based on RGB and depth (RGB-D) images to improve object recognition. In particular, our framework has two modules: (1) RGB and depth images are represented by convolutional-recursive neural networks to construct high level features, respectively;(2) co-training is exploited to make full use of unlabeled RGB-D instances due to the existing two independent views. Experiments on the standard RGB-D object dataset demonstrate that our method can compete against with other state-of-the-art methods with only 20% labeled data.

关键词： Accuracy Cameras Feature extraction Object recognition Semisupervised learning Shape Training

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A decorrelation approach for pruning of multilayer perceptron networks 6

引用

6th iapr TC3 International workshop on artificial neural networks for pattern recognition, ANNPR 2014

作者： Abbas, Hazem M. The German University in Cairo Cairo Egypt

ISBN: (纸本)9783319116556

In this paper, the architecture selection of a three-layer nonlinear feedforward network with linear output neurons and sigmoidal hidden neurons is carried out. In the proposed method, the conventional back propagation (BP) learning algorithm is used to train the network by minimizing the representation error. A new pruning algorithm employing statistical analysis can quantify the importance of each hidden unit. This is accomplished by providing lateral connections among the neurons of the hidden layer and minimizing the variance of the hidden neurons. Variance minimization has resulted in decorrelated neurons and thus the learning rule for the lateral connections in the hidden layer becomes a variation of the anti-Hebbian learning. The decorrelation process minimizes any redundant information transferred among the hidden neurons and therefore enables the network to capture the statistical properties of the required input-output mapping using the minimum number of hidden nodes. Hidden nodes with least contribution to the error minimization at the output layer will be pruned. Experimental results show that the proposed pruning algorithm correctly prunes irrelevant hidden units. © Springer International Publishing Switzerland 2014.

关键词： Neurons

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Semi-Supervised Dictionary Learning of Sparse Representations for Emotion recognition 1

引用

2nd iapr International workshop on Partially Supervised Learning (PSL)

作者： Kaechele, Markus Schwenker, Friedhelm Univ Ulm Inst Neural Informat Proc D-89069 Ulm Germany

ISBN: (数字)9783642407055

ISBN: (纸本)9783642407055;9783642407048

This work presents a technique for the classification of emotions in human-computer interaction. Based on biophysiological data, a dictionary learning approach is used to generate sparse representations of blood volume pulse signals. Such features are then used for classification of the current emotion. Unlabeled data, i.e. data without information about class membership, is used to enrich the dictionary learning stage. Superior representation abilities of the underlying structure of the data are demonstrated by the learnt dictionaries. As a result, classification rates are improved. Experimental validation in the form of different classification experiments is presented. The results are presented with a discussion about the benefits of the approach and the existing limitations.

关键词： Emotion recognition

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The effect of fuzzy training targets on voice quality classification

The effect of fuzzy training targets on voice quality classi...

引用

1st iapr TC3 workshop on Multimodal pattern recognition of Social Signals in Human-Computer-Interaction, MPRSS 2012

作者： Scherer, Stefan Kane, John Gobl, Christer Schwenker, Friedhelm Institute of Creative Technologies University of Southern California United States Phonetics and Speech Laboratory Trinity College Dublin Ireland Institute of Neural Information Processing Ulm University Germany

ISBN: (纸本)9783642370809

The dynamic use of voice qualities in spoken language can reveal useful information on a speaker's attitude, mood and affective states. This information may be desirable for a range of speech technology applications. However, annotation of voice quality may frequently be inconsistent across raters. But whom should one trust or is the truth somewhere in between? The current study looks first to describe a voice quality feature set that is suitable for differentiating voice qualities on a tense to breathy dimension. These features are used as inputs to a fuzzy-input fuzzy-output support vector machine (F 2SVM) algorithm, to automatically classify the voice qualities. The F2SVM is compared to standard approaches and shows promising results. Performances for cross validation, leave one speaker out, and cross corpus experiments of around 90% are achieved. © 2013 Springer-Verlag.

关键词： artificial intelligence

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Meta-Learning of Exploration and Exploitation Parameters with Replacing Eligibility Traces 1

引用

2nd iapr International workshop on Partially Supervised Learning (PSL)

作者： Tokic, Michel Schwenker, Friedhelm Palm, Guenther Univ Ulm Inst Neural Informat Proc D-89069 Ulm Germany Univ Appl Sci Inst Appl Res D-88250 Ravensburg Weingarten Weingarten Germany

ISBN: (数字)9783642407055

ISBN: (纸本)9783642407055;9783642407048

When developing autonomous learning agents, the performance depends crucially on the selection of reasonable learning parameters, for example learning rates or exploration parameters. In this work we investigate meta-learning of exploration parameters by using the "REINFORCE exploration control" (REC) framework, and combine REC with replacing eligibility traces, which are a basic mechanism for tackling the problem of delayed rewards in reinforcement learning. We show empirically for a robot example and the mountain-car problem with two goals how the proposed combination can help to improve learning performance. Furthermore, we also observe that the setting of time constant lambda is not straightforward, because it is intimately interrelated with the learning rate alpha.

关键词： Reinforcement learning

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Partially Supervised Anomaly Detection Using Convex Hulls on a 2D Parameter Space 1

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2nd iapr International workshop on Partially Supervised Learning (PSL)

作者： Costa, Gabriel B. P. Ponti, Moacir Frery, Alejandro C. Univ Sao Paulo Inst Ciencias Matemat & Comp BR-13566590 Sao Carlos SP Brazil Univ Fed Alagoas BR-13566590 Maceio AL Brazil

ISBN: (数字)9783642407055

ISBN: (纸本)9783642407055;9783642407048

Anomaly detection is the problem of identifying objects appearing to be inconsistent with the remainder of that set of data. Detecting such samples is useful on various applications such as fault detection, fraud detection and diagnostic systems. Partially supervised methods for anomaly detection are interesting because they only need data labeled as one of the classes (normal or abnormal). In this paper, we propose a partially supervised framework for anomaly detection based on convex hulls in a parameter space, assuming a given probability distribution. It can be considered a framework since it supports any model for the "normal" samples. We investigate an algorithm based on this framework, assuming the Gaussian distribution for the not anomalous ("normal") data, and compared the results with the One-class SVM and Naive Bayes classifiers, as well as two statistical anomaly detectors. The proposed method shows accuracy results that are comparable or better than the competing methods. Furthermore, this approach can handle any probability distribution or mixture of distributions, allowing the user to choose a parameter space that models adequately the problem of finding anomalies.

关键词： Anomaly Outlier Semi-supervised learning

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Bag-of-Words Against Nearest-Neighbor Search for Visual Object Retrieval

Bag-of-Words Against Nearest-Neighbor Search for Visual Obje...

引用

Asian Conference on pattern recognition (ACPR)

作者： Cai-Zhi Zhu Xiao Zhou Shin'Ichi Satoh National Institute of Informatics Japan Hefei Normal University China

We compare the Bag-of-Words (BoW) framework with the Approximate Nearest-Neighbor (ANN) based system in the context of visual object retrieval. This comparison is motivated by the implicit connection between these two methods: generally speaking, the BoW framework can be regarded as a quantization-guided ANN voting system. The value of establishing such comparison lies in: first, by comparing with other quantization-free ANN system, the performance loss caused by the quantization error in the BoW framework can be estimated quantitatively. Second, this comparison completely inspects the pros and cons of both ANN and BoW methods, thus to facilitate new algorithm design. In this study, by taking an independent dataset as the reference to validate matches, we design an ANN voting system that outperforms all other methods. Comprehensive and computationally intensive experiments are conducted on two Oxford datasets and two TrecVid instance search datasets, and the new state-of-the-art is achieved.

关键词： artificial neural networks Visualization Quantization (signal) Vectors Vocabulary Equations Context

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Robustness of a CAD system on digitized mammograms

Robustness of a CAD system on digitized mammograms

引用

5th INNS iapr TC 3 GIRPR workshop on artificial neural networks for pattern recognition, ANNPR 2012

作者： García-Manso, Antonio García-Orellana, Carlos J. Gallardo-Caballero, Ramón Lanconelli, Nico González-Velasco, Horacio Macías-Macías, Miguel University of Extremadura Avenida de Elvas s/n. Badajoz Extremadura Spain Medical Imaging Group Physics Dpt. Bologna University Viale B. Pichat 6/2 Bologna 40127 Italy

ISBN: (纸本)9783642332111

In this paper we study the robustness of our CAD system, since this is one of the main factors that determine its quality. A CAD system must guarantee consistent performance over time and in various clinical situations. Our CAD system is based on the extraction of features from the mammographic image by means of Independent Component Analysis, and machine learning classifiers, such as neural networks and Support Vector Machine. To measure the robustness of our CAD system we have used the digitized mammograms of the USF's DDSM database, because this database was built by digitizing mammograms from four different institutions (four different scanner) during more than 10 years. Thus, we can use the mammograms digitized with one scanner to train the system and the remaining to evaluate the performance, what gives us a measure of the robustness of our CAD system. © 2012 Springer-Verlag.

关键词： Independent component analysis

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