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检索条件"任意字段=8th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2012"

共 106 条记录，以下是31-40 订阅

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Autoencoder Attractors for Uncertainty Estimation 26

Autoencoder Attractors for Uncertainty Estimation

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Da Cruz, Steve Dias Taetz, Bertram Stifter, thomas Stricker, Didier IEE SA Bissen Luxembourg Univ Kaiserslautern Kaiserslautern Germany German Res Ctr Artificial Intelligence DFKI Darmstadt Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

the reliability assessment of a machine learning model's prediction is an important quantity for the deployment in safety critical applications. Not only can it be used to detect novel sceneries, either as out-of-distribution or anomaly sample, but it also helps to determine deficiencies in the training data distribution. A lot of promising research directions have either proposed traditional methods like Gaussian processes or extended deep learning based approaches, for example, by interpreting them from a Bayesian point of view. In this work we propose a novel approach for uncertainty estimation based on autoencoder models: the recursive application of a previously trained autoencoder model can be interpreted as a dynamical system storing training examples as attractors. While input images close to known samples will converge to the same or similar attractor, input samples containing unknown features are unstable and converge to different training samples by potentially removing or changing characteristic features. the use of dropout during training and inference leads to a family of similar dynamical systems, each one being robust on samples close to the training distribution but unstable on new features. Either the model reliably removes these features or the resulting instability can be exploited to detect problematic input samples. We evaluate our approach on several dataset combinations as well as on an industrial application for occupant classification in the vehicle interior for which we additionally release a new synthetic dataset.

关键词： Training Visualization Uncertainty Computational modeling Estimation Training data Safety

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Towards Automatic Parsing of Structured Visual Content through the Use of Synthetic data 26

Towards Automatic Parsing of Structured Visual Content throu...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Schoelch, Lukas Steinhaeuser, Jonas Beichter, Maximilian Seibold, Constantin Yang, Kailun Knaeble, Merlin Schwarz, thorsten Maedche, Alexander Stiefelhagen, Rainer Karlsruhe Inst Technol Karlsruhe Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Structured Visual Content (SVC) such as graphs, flow charts, or the like are used by authors to illustrate various concepts. While such depictions allow the average reader to better understand the contents, images containing SVCs are typically not machine-readable. this, in turn, not only hinders automated knowledge aggregation, but also the perception of displayed information for visually impaired people. In this work, we propose a synthetic dataset, containing SVCs in the form of images as well as ground truths. We show the usage of this dataset by an application that automatically extracts a graph representation from an SVC image. this is done by training a model via common supervised learning methods. As there currently exist no large-scale public datasets for the detailed analysis of SVC, we propose the Synthetic SVC (SSVC) dataset comprising 12,000 images with respective bounding box annotations and detailed graph representations. Our dataset enables the development of strong models for the interpretation of SVCs while skipping the time-consuming dense data annotation. We evaluate our model on both synthetic and manually annotated data and show the transferability of synthetic to real via various metrics, given the presented application. Here, we evaluate that this proof of concept is possible to some extend and lay down a solid baseline for this task. We discuss the limitations of our approach for further improvements. Our utilized metrics can be used as a tool for future comparisons in this domain. To enable further research on this task, the dataset is publicly available at https://***/3jN1pJJ.

关键词： Training Measurement Visualization Annotations Supervised learning Static VAr compensators Solids

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Factorization Approach for Sparse Spatio-Temporal Brain-Computer Interface 26

Factorization Approach for Sparse Spatio-Temporal Brain-Comp...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Lee, Byeong-Hoo Cho, Jeong-Hyun Kwon, Byoung-Hee Lee, Seong-Whan Korea Univ Dept Brain & Cognit Engn Seoul South Korea Korea Univ Dept Artificial Intelligence Seoul South Korea

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Recently, advanced technologies have unlimited potential in solving various problems with a large amount of data. However, these technologies have yet to show competitive performance in brain-computer interfaces (BCIs) which deal with brain signals. Basically, brain signals are difficult to collect in large quantities, in particular, the amount of information would be sparse in spontaneous BCIs. In addition, we conjecture that high spatial and temporal similarities between tasks increase the prediction difficulty. We define this problem as sparse condition. To solve this, a factorization approach is introduced to allow the model to obtain distinct representations from latent space. To this end, we propose two feature extractors: A class-common module is trained through adversarial learning acting as a generator;Class-specific module utilizes loss function generated from classification so that features are extracted with traditional methods. To minimize the latent space shared by the class-common and class-specific features, the model is trained under orthogonal constraint. As a result, EEG signals are factorized into two separate latent spaces. Evaluations were conducted on a single-arm motor imagery dataset. From the results, we demonstrated that factorizing the EEG signal allows the model to extract rich and decisive features under sparse condition.

关键词： Training Face recognition Feature extraction Brain modeling Electroencephalography Brain-computer interfaces Adversarial machine learning

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Boosting the Discriminant Power of Naive Bayes 26

Boosting the Discriminant Power of Naive Bayes

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Wang, Shihe Ren, Jianfeng Lian, Xiaoyu Bai, Ruibin Jiang, Xudong Univ Nottingham Sch Comp Sci Ningbo Peoples R China Ningbo Beilun Taihe High Sch Ningbo Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Naive Bayes has been widely used in many applications because of its simplicity and ability in handling both numerical data and categorical data. However, lack of modeling of correlations between features limits its performance. In addition, noise and outliers in the real-world dataset also greatly degrade the classification performance. In this paper, we propose a feature augmentation method employing a stack auto-encoder to reduce the noise in the data and boost the discriminant power of naive Bayes. the proposed stack auto-encoder consists of two auto-encoders for different purposes. the first encoder shrinks the initial features to derive a compact feature representation in order to remove the noise and redundant information. the second encoder boosts the discriminant power of the features by expanding them into a higher-dimensional space so that different classes of samples could be better separated in the higher-dimensional space. By integrating the proposed feature augmentation method with the regularized naive Bayes, the discrimination power of the model is greatly enhanced. the proposed method is evaluated on a set of machine-learning benchmark datasets. the experimental results show that the proposed method significantly and consistently outperforms the state-of-the-art naive Bayes classifiers.

关键词： Correlation Codes Benchmark testing Feature extraction Boosting data models Numerical models

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Deep Saliency Map Generators for Multispectral Video Classification 26

Deep Saliency Map Generators for Multispectral Video Classif...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Bayer, Jens Muench, David Arens, Michael Fraunhofer Ctr Machine Learning & Fraunhofer IOSB Ettlingen Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Despite their black box nature, deep neural networks have been successfully used in practical applications lately. In areas where the results of these applications can lead to safety hazards or decisions of ethical relevance, the application provider is accountable for the resulting decisions and should therefore be able to explain, how, and why a specific decision was made. For image processing networks, saliency map generators are a possible solution. A saliency map gives a visual hint on what is of special importance for the network's decision, can reveal possible dataset biases and give a more profound insight in the decision process of the black box. this paper investigates how 2D saliency map generators need to be adapted for 3D input data, and additionally, how the methods behave when applied not only to ordinary video input but rather multispectral 3D input data. this is exemplarily shown on 3D video input data in human action recognition in the infrared and visual spectrum and evaluated by using the insertion and deletion metrics. the dataset used in this work is the Multispectral Action dataset, where each scene is available in the long-wave infrared as well as the visual spectrum. To be able to draw a more general conclusion, the two investigated networks, 3D-ResNet 18 and Persistent Appearance Network (PAN), follow a different mindset. It could be shown, that the saliency methods can also be applied to 3D input data with remarkable results. the results show that a combined training with both, infrared and RGB 3D input data, lead to more focused saliency maps and outperform a training with only RGB or infrared data.

关键词： Measurement Training Visualization three-dimensional displays TV Neural networks Generators

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Automatic Foot Ulcer Segmentation Using an Ensemble of Convolutional Neural Networks 26

Automatic Foot Ulcer Segmentation Using an Ensemble of Convo...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Mahbod, Amirreza Schaefer, Gerald Ecker, Rupert Ellinger, Isabella Med Univ Vienna Inst Pathophysiol & Allergy Res Vienna Austria Loughborough Univ Dept Comp Sci Loughborough Leics England TissueGnost GmbH Dept Res & Dev Vienna Austria

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Foot ulcer is a common complication of diabetes mellitus and, associated with substantial morbidity and mortality, remains a major risk factor for lower leg amputations. Extracting accurate morphological features from foot wounds is crucial for appropriate treatment. Although visual inspection by a medical professional is the common approach for diagnosis, this is subjective and error-prone, and computer-aided approaches thus provide an interesting alternative. Deep learning-based methods, and in particular convolutional neural networks (CNNs), have shown excellent performance for various tasks in medical image analysis including medical image segmentation. In this paper, we propose an ensemble approach based on two encoder-decoder-based CNN models, namely LinkNet and U-Net, to perform foot ulcer segmentation. To deal with a limited number of available training samples, we use pretrained weights (EfficientNetB1 for the LinkNet model and EfficientNetB2 for the U-Net model) and perform further pre-training using the Medetec dataset while also applying a number of morphological-based and colour-based augmentation techniques. To boost the segmentation performance, we incorporate five-fold cross-validation, test time augmentation and result fusion. Applied on the publicly available chronic wound dataset and the MICCAI 2021 Foot Ulcer Segmentation (FUSeg) Challenge, our method achieves state-of-the-art performance with data-based Dice scores of 92.07% and 88.80%, respectively, and is the top ranked method in the FUSeg challenge leaderboard. the Dockerised guidelines, inference codes and saved trained models are publicly available at https://***/masih4/Foot Ulcer Segmentation.

关键词： Foot ulcer medical image analysis image segmentation machine learning deep learning ensemble

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PRNU-Net: a Deep learning Approach for Source Camera Model Identification based on Videos Taken with Smartphone 26

PRNU-Net: a Deep Learning Approach for Source Camera Model I...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Akbari, Younes Almaadeed, Noor Al-Maadeed, Somaya Khelifi, Fouad Bouridane, Ahmed Qatar Univ Dept Comp Sci & Engn Doha Qatar Northumbria Univ Dept Comp & Informat Sci London England Univ Sharjah Ctr Data Analyt & Cybernet Sharjah U Arab Emirates

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Recent advances in digital imaging have meant that every smartphone has a video camera that can record high-quality video for free and without restrictions. In addition, rapidly developing Internet technology has contributed significantly to the widespread distribution of digital video via web-based multimedia systems and mobile applications such as YouTube, Facebook, Twitter, WhatsApp, etc. However, as the recording and distribution of digital video has become affordable nowadays, security issues have become threatening and have spread worldwide. One of the security issues is the identification of source cameras on videos. Generally, two common categories of methods are used in this area, namely Photo Response Non-Uniformity (PRNU) and machine learning approaches. To exploit the power of both approaches, this work adds a new PRNU-based layer to a convolutional neural network (CNN) called PRNU-Net. To explore the new layer, the main structure of the CNN is based on the MISLnet, which has been used in several studies to identify the source camera. the experimental results show that the PRNU-Net is more successful than the MISLnet and that the PRNU extracted by the layer from low features, namely edges or textures, is more useful than high and mid-level features, namely parts and objects, in classifying source camera models. On average, the network improves the results in a new database by about 4%.

关键词： Deep learning Video on demand Social networking (online) databases Feature extraction Cameras Propagation losses

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Resume Parsing and Skill Extraction using Custom pattern Matching algorithm and Gemini API

Resume Parsing and Skill Extraction using Custom Pattern Mat...

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international conference on Computing Communication Control and Automation (ICCUBEA)

作者： Atharva Joshi Shailaja Pede Prathmesh Kolekar Aditya Metkar Department of Computer Science Engineering (Artificial Intelligence and Machine Learning) PCCOE SPPU India Pune India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

In the realm of contemporary recruitment, efficient resume parsing is essential for talent acquisition. this paper investigates resume parsing using advanced OCR technology and NLP methods. We begin with preprocessing, enhancing scanned resumes for legibility. Leveraging the Tesseract OCR engine, we convert images to machine-readable text. through NLP techniques with spaCy, we extract key information like personal details, education, experience, and *** approach integrates the Gemini Pro Vision API for semantic analysis and question-answering. this enriches our framework, enabling deeper insights and implicit attribute inference. Empirical validation across diverse datasets demonstrates the robustness of our methodology. this work sheds light on performance metrics and challenges in automated resume parsing, paving the way for future enhancements.

关键词： Visualization Technological innovation machine learning algorithms Resumes Optical character recognition Semantics Robustness data mining pattern matching Recruitment

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Recommending Question-Answers for Enriching Textbooks 8th

Recommending Question-Answers for Enriching Textbooks

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8th international conference on Big data Analytics (BDA)

作者： Kumar, Shobhan Chauhan, Arun IIIT Dharwad Dharwad Karnataka India

ISBN: (纸本)9783030666644;9783030666651

the students often use the community question-answering (cQA) systems along with textbooks to gain more knowledge. the millions of question-answers (QA) already accessible in these cQA forums. the huge amount of QA makes it hard for the students to go through all possible QA for a better understanding of the concepts. To address this issue, this paper provides a technological solution "Topic-based text enrichment process" for a textbook with relevant QA pairs from cQA. We used techniques of natural language processing, topic modeling, and data mining to extract the most relevant QA sets and corresponding links of cQA to enrich the textbooks. this work provides all the relevant QAs for the important topics of the textbook to the students, therefore it helps them to learn the concept more quickly. Experiments were carried out on a variety of textbooks such as pattern recognition and machine learning by Christopher M Bishop, data mining Concepts & techniques by Jiawei Han, Information theory, Inference, & learning Algorithms by David J.C. MacKay, and National Council of Educational Research and Training (NCERT). the results prove that we are effective in learning enhancement by enhancing the textbooks on various subjects and across different grades with the relevant QA pairs using automated techniques. We also present the results of quiz sessions which were conducted to evaluate the effectiveness of the proposed model in establishing relevance to learning among students.

关键词： Community question-answers (cQA) Relevance Text-enrichment Question-answers (QA) Topic modeling data-preprocessing e-learning

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8th international conference on Analysis of Images, Social Networks and Texts, AIST 2019

8th International Conference on Analysis of Images, Social N...

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8th international conference on Analysis of Images, Social Networks and Texts, AIST 2019

ISBN: (纸本)9783030395742

the proceedings contain 37 papers. the special focus in this conference is on international conference on Analysis of Images, Social Networks and Texts. the topics include: Automated approach to rhythm figures search in english text;Adapting the Graph2Vec approach to dependency trees for NLP tasks;recognition of parts of speech using the vector of bigram frequencies;formalization of medical records using an ontology: Patient complaints;a deep learning method study of user interest classification;morpheme segmentation for russian: Evaluation of convolutional neural network models;Using pre-trained deeply contextual model BERT for Russian named entity recognition;expert assessment of synonymic rows in ruwordnet;Text mining for evaluation of candidates based on their CVs;evolutionary algorithms for constructing an ensemble of decision trees;vec2graph: A python library for visualizing word embeddings as graphs;how to prevent harmful information spreading in social networks using simulation tools;effect of social graph structure on the utilization rate in a flat organization;discernment of smoke in image frames;Face recognition using DELF feature descriptors on RGB-D data;efficient information support of the automatic process and production control system;an ensemble of learning machine models for plant recognition;hand gestures detection, tracking and classification using convolutional neural network;synthesizing data using variational autoencoders for handling class imbalanced deep learning;new approach for fast residual strain estimation through rational 2D diffraction pattern processing;an algorithm for constructing a topological skeleton for semi-structured spatial data based on persistent homology;fast identification of fingerprint.

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