Inter-modality registration of NMRi and histological section images using neural networks regression in Gabor feature space

2009 ◽  
Author(s):  
Felix Bollenbeck ◽  
Rainer Pielot ◽  
Diana Weier ◽  
Winfriede Weschke ◽  
Udo Seiffert
Keyword(s):  
Neural Networks ◽  
Histological Section ◽  
Feature Space ◽  
Gabor Feature

scholarly journals Combine and conquer: event reconstruction with Bayesian Ensemble Neural Networks

2021 ◽  
Vol 2021 (4) ◽  
Author(s):  
Jack Y. Araz ◽  
Michael Spannowsky
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Top Quark ◽  
Feature Space ◽  
Training Sample ◽  
Bayesian Techniques ◽  
Training Sample Size ◽  
Event Reconstruction ◽  
Neural Network Classifiers ◽  
Quark Jets

Abstract Ensemble learning is a technique where multiple component learners are combined through a protocol. We propose an Ensemble Neural Network (ENN) that uses the combined latent-feature space of multiple neural network classifiers to improve the representation of the network hypothesis. We apply this approach to construct an ENN from Convolutional and Recurrent Neural Networks to discriminate top-quark jets from QCD jets. Such ENN provides the flexibility to improve the classification beyond simple prediction combining methods by linking different sources of error correlations, hence improving the representation between data and hypothesis. In combination with Bayesian techniques, we show that it can reduce epistemic uncertainties and the entropy of the hypothesis by simultaneously exploiting various kinematic correlations of the system, which also makes the network less susceptible to a limitation in training sample size.

scholarly journals Exploiting node metadata to predict interactions in large networks using graph embedding and neural networks

2021 ◽  
Author(s):  
Rogini Runghen ◽  
Daniel B Stouffer ◽  
Giulio Valentino Dalla Riva
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Link Prediction ◽  
Graph Embedding ◽  
Feature Space ◽  
Machine Learning Techniques ◽  
Large Networks ◽  
Data Set ◽  
Learning Techniques ◽  
Low Dimensional

Collecting network interaction data is difficult. Non-exhaustive sampling and complex hidden processes often result in an incomplete data set. Thus, identifying potentially present but unobserved interactions is crucial both in understanding the structure of large scale data, and in predicting how previously unseen elements will interact. Recent studies in network analysis have shown that accounting for metadata (such as node attributes) can improve both our understanding of how nodes interact with one another, and the accuracy of link prediction. However, the dimension of the object we need to learn to predict interactions in a network grows quickly with the number of nodes. Therefore, it becomes computationally and conceptually challenging for large networks. Here, we present a new predictive procedure combining a graph embedding method with machine learning techniques to predict interactions on the base of nodes' metadata. Graph embedding methods project the nodes of a network onto a---low dimensional---latent feature space. The position of the nodes in the latent feature space can then be used to predict interactions between nodes. Learning a mapping of the nodes' metadata to their position in a latent feature space corresponds to a classic---and low dimensional---machine learning problem. In our current study we used the Random Dot Product Graph model to estimate the embedding of an observed network, and we tested different neural networks architectures to predict the position of nodes in the latent feature space. Flexible machine learning techniques to map the nodes onto their latent positions allow to account for multivariate and possibly complex nodes' metadata. To illustrate the utility of the proposed procedure, we apply it to a large dataset of tourist visits to destinations across New Zealand. We found that our procedure accurately predicts interactions for both existing nodes and nodes newly added to the network, while being computationally feasible even for very large networks. Overall, our study highlights that by exploiting the properties of a well understood statistical model for complex networks and combining it with standard machine learning techniques, we can simplify the link prediction problem when incorporating multivariate node metadata. Our procedure can be immediately applied to different types of networks, and to a wide variety of data from different systems. As such, both from a network science and data science perspective, our work offers a flexible and generalisable procedure for link prediction.

scholarly journals Uncertainty-Aware Deep Classifiers Using Generative Models

2020 ◽  
Vol 34 (04) ◽  
pp. 5620-5627 ◽  
Author(s):  
Murat Sensoy ◽  
Lance Kaplan ◽  
Federico Cerutti ◽  
Maryam Saleki
Keyword(s):  
Neural Networks ◽  
Epistemic Uncertainty ◽  
Feature Space ◽  
Generative Models ◽  
Detection Methods ◽  
Generative Adversarial Networks ◽  
Data Sets ◽  
Bayesian Approaches ◽  
Data Set ◽  
Auxiliary Data

Deep neural networks are often ignorant about what they do not know and overconfident when they make uninformed predictions. Some recent approaches quantify classification uncertainty directly by training the model to output high uncertainty for the data samples close to class boundaries or from the outside of the training distribution. These approaches use an auxiliary data set during training to represent out-of-distribution samples. However, selection or creation of such an auxiliary data set is non-trivial, especially for high dimensional data such as images. In this work we develop a novel neural network model that is able to express both aleatoric and epistemic uncertainty to distinguish decision boundary and out-of-distribution regions of the feature space. To this end, variational autoencoders and generative adversarial networks are incorporated to automatically generate out-of-distribution exemplars for training. Through extensive analysis, we demonstrate that the proposed approach provides better estimates of uncertainty for in- and out-of-distribution samples, and adversarial examples on well-known data sets against state-of-the-art approaches including recent Bayesian approaches for neural networks and anomaly detection methods.

Feature Extraction of Video Using Artificial Neural Network

2017 ◽  
Vol 11 (2) ◽  
pp. 25-40 ◽  
Author(s):  
Yoshihiro Hayakawa ◽  
Takanori Oonuma ◽  
Hideyuki Kobayashi ◽  
Akiko Takahashi ◽  
Shinji Chiba ◽  
...  
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Image Recognition ◽  
High Performance ◽  
Feature Space ◽  
Middle Layer ◽  
Image Feature ◽  
Consecutive Series ◽  
Form Analysis ◽  
Low Dimensional

In deep neural networks, which have been gaining attention in recent years, the features of input images are expressed in a middle layer. Using the information on this feature layer, high performance can be demonstrated in the image recognition field. In the present study, we achieve image recognition, without using convolutional neural networks or sparse coding, through an image feature extraction function obtained when identity mapping learning is applied to sandglass-style feed-forward neural networks. In sports form analysis, for example, a state trajectory is mapped in a low-dimensional feature space based on a consecutive series of actions. Here, we discuss ideas related to image analysis by applying the above method.

scholarly journals Multiple Network Fusion with Low-Rank Representation for Image-Based Age Estimation

2018 ◽  
Vol 8 (9) ◽  
pp. 1601
Author(s):  
Chaoqun Hong ◽  
Zhiqiang Zeng ◽  
Xiaodong Wang ◽  
Weiwei Zhuang
Keyword(s):  
Neural Networks ◽  
Age Estimation ◽  
Feature Fusion ◽  
Estimation Method ◽  
Feature Space ◽  
Image Features ◽  
Low Rank ◽  
Face Images ◽  
Low Rank Representation ◽  
Multiple Network

Image-based age estimation is a challenging task since there are ambiguities between the apparent age of face images and the actual ages of people. Therefore, data-driven methods are popular. To improve data utilization and estimation performance, we propose an image-based age estimation method. Theoretically speaking, the key idea of the proposed method is to integrate multi-modal features of face images. In order to achieve it, we propose a multi-modal learning framework, which is called Multiple Network Fusion with Low-Rank Representation (MNF-LRR). In this process, different deep neural network (DNN) structures, such as autoencoders, Convolutional Neural Networks (CNNs), Recursive Neural Networks (RNNs), and so on, can be used to extract semantic information of facial images. The outputs of these neural networks are then represented in a low-rank feature space. In this way, feature fusion is obtained in this space, and robust multi-modal image features can be computed. An experimental evaluation is conducted on two challenging face datasets for image-based age estimation extracted from the Internet Move Database (IMDB) and Wikipedia (WIKI). The results show the effectiveness of the proposed MNF-LRR.

scholarly journals Inter-Class Angular Loss for Convolutional Neural Networks

2019 ◽  
Vol 33 ◽  
pp. 3894-3901 ◽  
Author(s):  
Le Hui ◽  
Xiang Li ◽  
Chen Gong ◽  
Meng Fang ◽  
Joey Tianyi Zhou ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Deep Neural Networks ◽  
Learning Difficulties ◽  
Feature Space ◽  
Superior Performance ◽  
Strongly Correlated ◽  
Discriminative Ability ◽  
Practical Applications ◽  
Classification Tasks

Convolutional Neural Networks (CNNs) have shown great power in various classification tasks and have achieved remarkable results in practical applications. However, the distinct learning difficulties in discriminating different pairs of classes are largely ignored by the existing networks. For instance, in CIFAR-10 dataset, distinguishing cats from dogs is usually harder than distinguishing horses from ships. By carefully studying the behavior of CNN models in the training process, we observe that the confusion level of two classes is strongly correlated with their angular separability in the feature space. That is, the larger the inter-class angle is, the lower the confusion will be. Based on this observation, we propose a novel loss function dubbed “Inter-Class Angular Loss” (ICAL), which explicitly models the class correlation and can be directly applied to many existing deep networks. By minimizing the proposed ICAL, the networks can effectively discriminate the examples in similar classes by enlarging the angle between their corresponding class vectors. Thorough experimental results on a series of vision and nonvision datasets confirm that ICAL critically improves the discriminative ability of various representative deep neural networks and generates superior performance to the original networks with conventional softmax loss.

Multi-Phase Texture Segmentation Using Gabor Features Histograms Based on Wasserstein Distance

2014 ◽  
Vol 15 (5) ◽  
pp. 1480-1500 ◽  
Author(s):  
Motong Qiao ◽  
Wei Wang ◽  
Michael Ng
Keyword(s):  
Gabor Filter ◽  
Error Function ◽  
Feature Space ◽  
Wasserstein Distance ◽  
Energy Functional ◽  
Phase Image ◽  
Minimization Method ◽  
Closed Form Solutions ◽  
Gabor Feature ◽  
Multi Phase

AbstractWe present a multi-phase image segmentation method based on the histogram of the Gabor feature space, which consists of a set of Gabor-filter responses with various orientations, scales and frequencies. Our model replaces the error function term in the original fuzzy region competition model with squared 2-Wasserstein distance function, which is a metric to measure the distance of two histograms. The energy functional is minimized by alternative minimization method and the existence of closed-form solutions is guaranteed when the exponent of the fuzzy membership term being 1 or 2. We test our model on both simple synthetic texture images and complex natural images with two or more phases. Experimental results are shown and compared to other recent results.

GAID: Genetic Adaptive Incident Detection for Freeways

2003 ◽  
Vol 1856 (1) ◽  
pp. 96-105 ◽  
Author(s):  
Prasenjit Roy ◽  
Baher Abdulhai
Keyword(s):  
Neural Networks ◽  
Feature Space ◽  
Detection Algorithm ◽  
Incident Detection ◽  
Time Step ◽  
Detection Algorithms ◽  
Bayesian Update ◽  
Average Condition ◽  
The Difference ◽  
Input Variables

Extensive research on point-detector-based automatic traffic-impeding incident detection indicates the potential superiority of neural networks over conventional approaches. All approaches, however, including neural networks, produce detection algorithms that are location specific—that is, neither transferable nor adaptive. A recently designed and ready-to-implement freeway incident detection algorithm based on genetically optimized probabilistic neural networks (PNN) is presented. The combined use of genetic algorithms and neural networks produces GAID, a genetic adaptive incident detection logic that uses flow and occupancy values from the upstream and downstream loop detector stations to automatically detect an incident between the said stations. As input, GAID uses modified input feature space based on the difference of the present volume and occupancy condition from the average condition for time and location. On the output side, it uses a Bayesian update process and converts isolated binary outputs into a continuous probabilistic measure—that is, updated every time step. GAID implements genetically optimized separate smoothing parameters for its input variables, which in turn increase the overall generalization accuracy of the detector algorithm. The detector was subjected to off-line tests with real incident data from a number of freeways in California. Results and further comparison with the McMaster algorithm indicate that GAID with a PNN core has a better detection rate and a lower false alarm rate than the PNN alone and the well-established McMaster algorithm. Results also indicate that the algorithm is the least location specific, and the automated genetic optimization process makes it adapt to new site conditions.

Somatic Cells Recognition by Application of Gabor Feature-Based (2D)2PCA

2017 ◽  
Vol 31 (12) ◽  
pp. 1757009 ◽  
Author(s):  
Xiaojing Gao ◽  
Heru Xue ◽  
Xin Pan ◽  
Xinhua Jiang ◽  
Yanqing Zhou ◽  
...  
Keyword(s):  
Gabor Filter ◽  
Somatic Cells ◽  
Bovine Mastitis ◽  
Principal Component ◽  
Feature Space ◽  
Support Vector ◽  
Large Set ◽  
Novel Approach ◽  
Gabor Feature ◽  
Feature Based

In this paper, we propose a novel approach of Gabor feature based on bi-directional two-dimensional principal component analysis ((2D)2PCA) for somatic cells recognition. Firstly, Gabor features of different orientations and scales are extracted by the convolution of Gabor filter bank. Secondly, dimensionality reduction of the feature space applies (2D)2PCA in both row and column. Finally, the classifier uses Support Vector Machine (SVM) to achieve our goal. The experimental results are obtained using a large set of images from different sources. The results of our proposed method are not only efficient in accuracy and speed, but also robust to illumination in bovine mastitis via optical microscopy.

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