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Introducing Bidirectional Ordinal Classifier Cascades Based on a Pain Intensity recognition Scenario 25th

Introducing Bidirectional Ordinal Classifier Cascades Based ...

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25th International Conference on pattern recognition Workshops, ICPR 2020

作者： Bellmann, Peter Lausser, Ludwig Kestler, Hans A. Schwenker, Friedhelm Institute of Neural Information Processing Ulm University James-Franck-Ring Ulm89081 Germany Institute of Medical Systems Biology Ulm University Albert-Einstein-Allee 11 Ulm89081 Germany

ISBN: (纸本)9783030687793

Ordinal classifier cascades (OCCs) are popular machine learning tools in the area of ordinal classification. OCCs constitute specific classification ensemble schemes that work in sequential manner. Each of the ensemble’s members either provides the architecture’s final prediction, or moves the current input to the next ensemble member. In the current study, we first confirm the fact that the direction of OCCs can have a high impact on the distribution of its predictions. Subsequently, we introduce and analyse our proposed bidirectional combination of OCCs. More precisely, based on a person-independent pain intensity scenario, we provide an ablation study, including the evaluation of different OCCs, as well as different popular error correcting output codes (ECOC) models. the provided outcomes show that our proposed straightforward approach significantly outperforms common OCCs, with respect to the accuracy and mean absolute error performance measures. Moreover, our results indicate that, while our proposed bidirectional OCCs are less complex in general, they are able to compete with and even outperform most of the analysed ECOC models. © 2021, Springer Nature Switzerland AG.

关键词： Health

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Relative Feature Importance

Relative Feature Importance

引用

International Conference on pattern recognition

作者： Gunnar König Christoph Molnar Bernd Bischl Moritz Grosse-Wentrup Research Group Neuroinformatics University of Vienna Institute for Statistics LMU Munich Vienna Cognitive Science Hub

Interpretable Machine Learning (IML) methods are used to gain insight into the relevance of a feature of interest for the performance of a model. Commonly used IML methods differ in whether they consider features of interest in isolation, e.g., Permutation Feature Importance (PFI), or in relation to all remaining feature variables, e.g., Conditional Feature Importance (CFI). As such, the perturbation mechanisms inherent to PFI and CFI represent extreme reference points. We introduce Relative Feature Importance (RFI), a generalization of PFI and CFI that allows for a more nuanced feature importance computation beyond the PFI versus CFI dichotomy. With RFI, the importance of a feature relative to any other subset of features can be assessed, including variables that were not available at training time. We derive general interpretation rules for RFI based on a detailed theoretical analysis of the implications of relative feature relevance, and demonstrate the method's usefulness on simulated examples.

关键词： Training Perturbation methods Machine learning pattern recognition

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Attention Stereo Matching Network

Attention Stereo Matching Network

引用

International Conference on pattern recognition

作者： Doudou Zhang Jing Cai Yanbing Xue Zan Gao Hua Zhang School of Computer Science and Engineering Tianjin University of Technology Tianjin China Institute of AI Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong China Tianjin Sino-German University of Applied Sciences Tianjin China

Despite great progress, previous stereo matching algorithms still lack the ability to match textureless regions and slender structure areas. To tackle this problem, we propose ASM-Net, an attention stereo matching network. Attention module and disparity refinement module are constructed in the ASMNet. the attention module can improve correlation information between two images by channels and spatial attention. the feature-guided disparity refinement module learns more geometry information in different feature levels to refine the coarse prediction resolution constantly. the proposed approach was evaluated on several benchmark datasets. Experiments show that the proposed method achieves competitive results on KITTI and Scene-Flow datasets while running in real-time at 14ms.

关键词： Geometry Correlation Benchmark testing Real-time systems pattern recognition Spatial resolution

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Towards Urban Tree recognition in Airborne Point Clouds with Deep 3D Single-Shot Detectors 25th

Towards Urban Tree Recognition in Airborne Point Clouds wit...

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25th International Conference on pattern recognition Workshops, ICPR 2020

作者： Schmohl, Stefan Kölle, Michael Frolow, Rudolf Soergel, Uwe University of Stuttgart Geschwister-Scholl-Str. 24D Stuttgart70174 Germany

ISBN: (纸本)9783030687861

Automatic mapping of individual urban trees is increasingly important to city administration and planing. Although deep learning algorithms are now standard methodology in computer vision, their adaption to individual tree detection in urban areas has hardly been investigated so far. In this work, we propose a deep single-shot object detection network to find urban trees in point clouds from airborne laser scanning. the network consists of a sparse 3D convolutional backbone for feature extraction and a subsequent single-shot region proposal network for the actual detection. It takes as input raw 3D voxel clouds, discretized from the point cloud in preprocessing. Outputs are cylindrical tree objects paired with their detection scores. We train and evaluate the network on the ISPRS Vaihingen 3D Benchmark dataset with custom tree object labels. the general feasibility of our approach is demonstrated. It achieves promising results compared to a traditional 2D baseline using watershed segmentation. We also conduct comparisons with state-of-the-art machine learning methods for semantic point segmentation. © 2021, Springer Nature Switzerland AG.

关键词： Object detection

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Cross-Lingual Text Image recognition via Multi-Task Sequence to Sequence Learning

Cross-Lingual Text Image Recognition via Multi-Task Sequence...

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International Conference on pattern recognition

作者： Zhuo Chen Fei Yin Xu-Yao Zhang Qing Yang Chena-Lin Liu School of Artificial Intelligence University of Chinese Academy of Sciences Beijing P.R. China CAS Center for Excellence in Brain Science and Intelligence Technology Beijing P.R. China

this paper considers recognizing texts shown in a source language and translating into a target language, without generating the intermediate source language text image recognition results. We call this problem Cross-Lingual Text Image recognition (CLTIR). To solve this problem, we propose a multi-task system containing a main task of CLTIR and an auxiliary task of Mono-Lingual Text Image recognition (MLTIR) simultaneously. Two different sequence to sequence learning methods, a convolution based attention model and a Bidirectional Long Short-Term Memory (BLSTM) model with Connectionist Temporal Classification (CTC), are adopted for these tasks respectively. We evaluate the system on a newly collected Chinese-English bilingual movie subtitle image dataset. Experimental results demonstrate the multi-task learning framework performs superiorly in both languages.

关键词： Training Learning systems Image recognition Text recognition Target recognition Convolution Semantics

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Fusion of Global-Local Features for Image Quality Inspection of Shipping Label

Fusion of Global-Local Features for Image Quality Inspection...

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International Conference on pattern recognition

作者： Sungho Suh Paul Lukowicz Yong Oh Lee TU Kaiserslautern Kaiserslautern Germany German Research Center for Artificial Intelligence (DFKI) Kaiserslautern Germany Smart Convergence Group Korea Institute of Science and Technology Europe Forschungsgesellschaft mbH Saarbrticken Germany

the demands of automated shipping address recognition and verification have increased to handle a large number of packages and to save costs associated with misdelivery. A previous study proposed a deep learning system where the shipping address is recognized and verified based on a camera image capturing the shipping address and barcode area. Because the system performance depends on the input image quality, inspection of input image quality is necessary for image preprocessing. In this paper, we propose an input image quality verification method combining global and local features. Object detection and scale-invariant feature transform in different feature spaces are developed to extract global and local features from several independent convolutional neural networks. the conditions of shipping label images are classified by fully connected fusion layers with concatenated global and local features. the experimental results regarding real captured and generated images show that the proposed method achieves better performance than other methods. these results are expected to improve the shipping address recognition and verification system by applying different image preprocessing steps based on the classified conditions.

关键词： Image quality Image recognition Text recognition System performance Transforms Object detection Inspection

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Aerial Road Segmentation in the Presence of Topological Label Noise

Aerial Road Segmentation in the Presence of Topological Labe...

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International Conference on pattern recognition

作者： Corentin Henry Friedrich Fraundorfer Eleonora Vig Remote Sensing Technology Institute German Aerospace Center (DLR) Oberpfaffenhofen Germany Institute of Computer Graphics and Vision Graz University of Technology (TUG) Graz Austria Amazon Berlin Germany

ISBN: (纸本)9781728188089;9781728188096

the availability of large-scale annotated datasets has enabled Fully-Convolutional Neural Networks to reach outstanding performance on road extraction in aerial images. However, high-quality pixel-level annotation is expensive to produce and even manually labeled data often contains topological errors. Trading off quality for quantity, many datasets rely on already available yet noisy labels, for example from OpenStreetMap. In this paper, we explore the training of custom U-Nets built with ResNet and DenseNet backbones using noise-aware losses that are robust towards label omission and registration noise. We perform an extensive evaluation of standard and noise-aware losses, including a novel Bootstrapped DICE-Coefficient loss, on two challenging road segmentation benchmarks. Our losses yield a consistent improvement in overall extraction quality and exhibit a strong capacity to cope with severe label noise. Our method generalizes well to two other fine-grained topology delineation tasks: surface crack detection for quality inspection and cell membrane extraction in electron microscopy imagery.

关键词： Training Image segmentation Roads Topology pattern recognition Noise measurement Electron microscopy

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Exploiting the Logits: Joint Sign Language recognition and Spell-Correction

Exploiting the Logits: Joint Sign Language Recognition and S...

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International Conference on pattern recognition

作者： Christina Runkel Stefan Dorenkamp Hartmut Bauermeister Michael Moeller Department for Computer Science and Electrical Engineering University of Siegen University of Siegen

Machine learning techniques have excelled in the automatic semantic analysis of images, reaching human-level performances on challenging benchmarks. Yet, the semantic analysis of videos remains challenging due to the significantly higher dimensionality of the input data, respectively, the significantly higher need for annotated training examples. By studying the automatic recognition of german sign language videos, we demonstrate that on the relatively scarce training data of 2.800 videos, modern deep learning architectures for video analysis (such as ResNeXt) along with transfer learning on large gesture recognition tasks, can achieve about 75% character accuracy. Considering that this leaves us with a probability of under 25% that a 5 letter word is spelled correctly, spell-correction systems are crucial for producing readable outputs. the contribution of this paper is to propose a convolutional neural network for spell-correction that expects the softmax outputs of the character recognition network (instead of a misspelled word) as an input. We demonstrate that purely learning on softmax inputs in combination with scarce training data yields overfitting as the network learns the inputs by heart. In contrast, training the network on several variants of the logits of the classification output i.e. scaling by a constant factor, adding of random noise, mixing of softmax and hardmax inputs or purely training on hardmax inputs, leads to better generalization while benefitting from the significant information hidden in these outputs (that have 98% top-5 accuracy), yielding a readable text despite the comparably low character accuracy.

关键词： Training Systematics Assistive technology Semantics Transfer learning Training data Gesture recognition

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End-to-End Hierarchical Relation Extraction for Generic Form Understanding

End-to-End Hierarchical Relation Extraction for Generic Form...

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International Conference on pattern recognition

作者： Tuan Anh Nguyen Dang Duc thanh Hoang Quang Bach Tran Chih-Wei Pan thanh Dat Nguyen Cinnamon AI Labs Hanoi Vietnam

ISBN: (纸本)9781728188089;9781728188096

Form understanding is a challenging problem which aims to recognize semantic entities from the input document and their hierarchical relations. Previous approaches face significant difficulty dealing with the complexity of the task, thus treat these objectives separately. To this end, we present a novel deep neural network to jointly perform both entity detection and link prediction in an end-to-end fashion. Our model extends the Multistage Attentional U-Net architecture with the Part-Intensity Fields and Part-association Fields for link prediction, enriching the spatial information flow with the additional supervision from entity linking. We demonstrate the effectiveness of the model on the Form Understanding in Noisy Scanned Documents (FUNSD) dataset, where our method substantially outperforms the original model and state-of-the-art baselines in both Entity Labeling and Entity Linking task.

关键词： Correlation Face recognition Semantics Neural networks Deep architecture Predictive models Noise measurement

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Emerging Relation Network and Task Embedding for Multi-Task Regression Problems

Emerging Relation Network and Task Embedding for Multi-Task ...

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International Conference on pattern recognition

作者： Jens Schreiber Bernhard Sick University of Kassel Kassel

Multi-task learning (MTL) provides state-of-the-art results in many applications of computer vision and natural language processing. In contrast to single-task learning (STL), MTL allows for leveraging knowledge between related tasks improving prediction results on all tasks. However, there is a limited number of comparative studies applied to MTL architectures for regression and time series problems taking recent advances of MTL into account. An intriguing, non-linear time-series problem are day ahead forecasts of the expected power generation for renewable power plants. therefore, the main contribution of this article is a comparative study of the following recent and relevant MTL architectures: Hard parameter sharing (HPS), cross-stitch network (CSN), and sluice network (SN). they are compared to a multi-layer perceptron (MLP) model of similar size in an STL setting. As a additional contribution, we provide a simple, yet practical approach to model task specific information through an embedding layer in an MLP, referred to as task embedding. Further, we contribute a new MTL architecture named emerging relation network (ERN), which can be considered as an extension of the SN. For a solar power dataset, the task embedding achieves the best mean improvement with 8.2%. For two wind and one additional solar dataset, the ERN is the best MTL architecture with improvements up to 11.3%.

关键词： Computer vision Time series analysis Computer architecture Natural language processing pattern recognition Task analysis Power generation

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