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Non-reference Image Quality Assessment for Contrast Distortion Based on Pixel Statistics and Color 2020

Non-reference Image Quality Assessment for Contrast Distorti...

引用

2020 international conference on pattern recognition and Intelligent Systems, PRIS 2020

作者： Huang, Ying Li, Bailun Jiang, Meilan Soft Engineering Institute Chong Qing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781450387699

For most natural images, proper contrast enhancement can achieve better visual quality. However, there are few image quality assessment methods for contrast distortion. We improve a new non-reference image quality assessment model to predict the image quality of contrast changes. Our improvements can be listed in two aspects:1. From the perspective of gray pixel information statistics, we add new perceptual features to the original model, including standard deviation, histogram energy, and skewness. These features enhance the prediction accuracy of the model. 2. Considering the effect of color on the contrast of the image, we extracted two key features related to the overall color of the image, named color saturation and colorfulness. Furthermore, support vector regression (SVR) is used to fuse all features to predict the image quality score, and we achieve better performance on three typical databases (CID2013, CCID2014, and CSIQ). © 2020 ACM.

关键词： Image quality

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An Efficient pattern recognition Based Method for Drug-Drug Interaction Diagnosis

An Efficient Pattern Recognition Based Method for Drug-Drug ...

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Artificial Intelligence and Data Analytics (CAIDA), international conference on

作者： Rabia Javed Tanzila Saba Salman Humdullah Nor Shahida Mohd Jamail Mazhar Javed Awan Lahore College for Women University Lahore Pakistan Computer and Information Science Prince Sultan University Riyadh Saudi Arabia Faculty of Computing Universiti Teknologi Malaysia Johor Bahru University of Management and Technology Lahore Pakistan

ISBN: (纸本)9781665430418

The diagnosis of interactions between two drugs is an essential procedure in drug development. Many medical tool's offer inclusive records related to DDI. However, this tool's results are not very satisfactory. The main aim is to propose an efficient approach based on pattern matching that identifies the interaction between two drugs. In this study, the goal is to collect the data from the DrugBank, which is a publicly available source. The drug-related data includes drug ID, drug names, and various kinds of sentences of drug-drug interaction. Drug names will be identified by drug names dictionary defined in the corpus, and sentences will be determined according to given patterns. These sentences will treat as input data, and preprocessing steps will perform in these sentences. Various types of features are selected for machine learning classification. Then all the attributes will be classified into desired classes. The proposed method gains 95.4% accuracy from the random forest classifier.

关键词： Drugs Dictionaries Data analysis Data preprocessing Random forests pattern matching Medical diagnostic imaging

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An Efficient Descriptor for Gait recognition Using Spatio-Temporal Cues 1st

An Efficient Descriptor for Gait Recognition Using Spatio-Te...

引用

1st international conference on Emerging Technology in Modelling and Graphics, IEMGraph 2018

作者： Gupta, Sanjay Kumar Sultaniya, Gaurav Mahesh Chattopadhyay, Pratik Varanasi India

ISBN: (纸本)9789811374029

In this paper, we introduce a new efficient feature for gait recognition, namely the boundary energy image (BEI). To construct this feature, the contours of the binary silhouettes in a gait sequence are extracted, and average of these contours is computed. Finally, the dimensionality of the feature set is reduced by applying PCA, and LDA is used to classify the reduced feature set. Evaluation of the proposed approach on CASIA B and TUMGAID data sets has shown significantly accurate results. Comparative study with existing techniques also show that our approach outperforms existing features like gait entropy image, active energy image and gait energy image by a substantially high margin both in terms of accuracy and response time. © Springer Nature Singapore Pte Ltd 2020.

关键词： pattern recognition

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Joint Extraction of Symptoms and Details in Chinese Medical Records Using the Overlapping Mechanism 16

Joint Extraction of Symptoms and Details in Chinese Medical ...

引用

16th IEEE international Wireless Communications and Mobile computing conference, IWCMC 2020

作者： Hu, Qian Dong, Wan Liu, Ning Wang, Jing Shen, Yanan Wu, Zhichao Ai Doctor Smart Healthcare Technology Ltd Beijing China National Lab. of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728131290

The description of the patient's symptoms and details in the Chinese electronic medical record is an important basis for assisting clinicians to make diagnosis and treatment decisions in the clinical decision system. Therefore, it is greatly significant to extract symptoms and details. We propose an end-to-end overlapping joint extraction method to complete the entity identification and relationship extraction of symptoms and details in the medical record. By overlapping annotation mechanism, this method uses word vector features, word segmentation features and medical dictionary features in the embedding layer of BiLSTM-CRF to implement the overlapped relationship extraction between entity identification and entity of symptom and detail in one step. The experimental results show that our method achieves 80% in F1 score on the real outpatient medical record test set, which outperforms the existed state-of-the-art methods. © 2020 IEEE.

关键词： Extraction

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Comparison of Backbones for Semantic Segmentation Network 5

Comparison of Backbones for Semantic Segmentation Network

引用

2020 5th international conference on Intelligent computing and Signal Processing, ICSP 2020

作者： Zhang, Rongyu Du, Lixuan Xiao, Qi Liu, Jiaming International School Beijing University of Post and Telecommunication Beijing100876 China International School of BusinessandFiance Sun Yat-Sen University Guangzhou510275 China Pattern Recognition and Intelligent System Lab Beijing University of Posts and Telecommunications Beijing100876 China

As for the classification network that is constantly emerging with each passing day, different classification network as the backbone of the semantic segmentation network may show different performance. This paper selected the road extraction data set of CVPR DeepGlobe, and compared the performance differences of VGG-16 as the backbone of Unet, ResNet34, ResNet101 and Xception as the backbone of AD-LinkNet. When VGG-16 is used as the backbone of the semantic segmentation network, it performs better in the face of long and wide road extraction. As the backbone of the semantic segmentation network, ResNet has a higher ability to extract small roads. When Xception is used as the backbone of the semantic segmentation network, it not only retains the characteristics of ResNet34, but also can effectively deal with the complex situation of extracting target covered by occlusions. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Semantic Segmentation

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Multiple speech source separation by using MVDR for B-format recordings 9

Multiple speech source separation by using MVDR for B-format...

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9th international conference on computing and pattern recognition, ICCPR 2020

作者： Jia, Yitian Jia, Maoshen Li, Lu Gao, Ju Yang, Shaoxin Faculty of Information Technology Beijing University of Technology 100 PingLeYuan Chaoyang District Beijing China Beijing-Dublin International College Beijing University of Technology 100 PingLeYuan Chaoyang District Beijing China

ISBN: (纸本)9781450387835

This paper presents a multiple speech source separation method that adopts the minimum variance distortionless response (MVDR) beamformer to separate speech sources based on the direction-of-arrival (DOA) estimated from the B-format microphone recordings. Unlike conventional microphone arrays, the B-format microphone provides complete spatial information of the pressure gradient and acoustic pressure which is useful to multiple sources localization. Therefore, this paper adopts the single-source points detection of the B-format microphone recordings, where only the dominant source is present in the time-frequency domain. A higher accuracy of source localization is achieved by identifying and utilizing only single-source points which contain correct direction cues. According to the DOA estimates, MVDR beamformer is then adopted for recovering the sources separately. The objective evaluation shows that the proposed method outperforms the existing BSS algorithm. © 2020 ACM.

关键词： Blind source separation

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Aggregation of LARK Vectors for Facial Image Classification

Aggregation of LARK Vectors for Facial Image Classification

引用

international conference on Mathematical Modelling and Scientific computing

作者： Vinay, A. Kamath, Vinayaka R. Varun, M. Nidheesh Natarajan, S. Murthy, K. N. B. PES Univ Ctr Pattern Recognit & Machine Intelligence Bengaluru India

ISBN: (纸本)9789811513381;9789811513374

Face recognition is prevailing to be a key aspect wherever there is a need for interaction between humans and machines. This can be achieved by containing a set of sketches for all the possible individuals and then cross-validating at necessary circumstances. We propose a mechanism to fulfil this task which is centred on locally adaptive regression kernels. A comparative study has been presented at encoding stages as well as at the classification stages of the pipeline. The results are cautiously examined and analyzed to deduce the best mechanism out of the proposed methodologies. All the ideologies have been tested for multiple iterations on benchmark datasets like ORL, grimace and faces 95. The vectorized descriptors have been subjected to encoding using slightly refined methods of feature aggregation and clustering to assist classifiers in imputing the test subjects to their respective classes. The encoded vectors are classified using Gaussian Naive Bayes, Stochastic Gradient Descent classifier, linear discriminant analysis and K Nearest Neighbour to accomplish face recognition. An inference on sparse nature of locally adaptive regression kernels was made from the experimentation. A rigorous study regarding the discrepancies of the performance of LARK descriptors is reported.

关键词： Adaptive Kernels Bayesian Classifier Feature aggregation Sparse features Image Classification

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Neural Network Optimization with Biologically Inspired Low-Dimensional Manifold Learning

Neural Network Optimization with Biologically Inspired Low-D...

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international conference on Computational Science and Computational Intelligence (CSCI)

作者： Hieu Le Andrew Wood Sylee Dandekar Peter Chin Dept. of Computer Science Boston University Boston USA

ISBN: (纸本)9781665458429

Neural Networks learn to recognize and leverage patterns in data. In most cases, while data is represented in a high-dimensional space, the patterns within the data exist along a manifold in a small subset of those dimensions. In this paper, we show that by using a biologically inspired algorithm called Geometric Multi-Resolution Analysis (GMRA), these low-dimensional manifolds can be computed and can be used to convert datasets into more useful forms for learning. We also show that, thanks to the lower-dimensional representation of the converted datasets, that smaller networks can achieve state-of-the-art performance while using significantly fewer parameters.

关键词： Manifolds Scientific computing Computational modeling Neural networks Biology pattern recognition Manifold learning

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Every Corporation Owns Its Image: Corporate Credit Ratings via Convolutional Neural Networks 6

Every Corporation Owns Its Image: Corporate Credit Ratings v...

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6th IEEE international conference on Computer and Communications, ICCC 2020

作者： Feng, Bojing Xue, Wenfang Xue, Bindang Liu, Zeyu Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Science Beijing China School of Astronautics Beihang University Beijing China Tianjin Academy for Intelligent Recognition Technologies Tianjin China

ISBN: (纸本)9781728186351

Credit rating is an analysis of the credit risks associated with a corporation, which reflect the level of the riskiness and reliability in investing. There have emerged many studies that implement machine learning techniques to deal with corporate credit rating. However, the ability of these models is limited by enormous amounts of data from financial statement reports. In this work, we analyze the performance of traditional machine learning models in predicting corporate credit rating. For utilizing the powerful convolutional neural networks and enormous financial data, we propose a novel end-to-end method, Corporate Credit Ratings via Convolutional Neural Networks, CCR-CNN for brevity. In the proposed model, each corporation is transformed into an image. Based on this image, CNN can capture complex feature interactions of data, which are difficult to be revealed by previous machine learning models. Extensive experiments conducted on the Chinese public-listed corporate rating dataset which we build, prove that CCR-CNN outperforms the state-of-the-art methods consistently. © 2020 IEEE.

关键词： Convolutional neural networks

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Decision fusion for partially occluded face recognition using common vector approach 14th

Decision fusion for partially occluded face recognition usin...

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14th international Scientific and Technical conference on Computer Science and Information Technologies, CSIT 2019

作者： Koc, Mehmet Bilecik Seyh Edebali University Bilecik Turkey

ISBN: (纸本)9783030336943

Partial occlusions in the face image negatively affect the performance of a face recognition system. Modular versions of some methods are used to overcome this problem. Modular Common Vector Approach (MCVA) was successfully applied partial occlusion problem. In this work, we apply some well-known decision fusion methods (product rule, borda count, and majority voting) to the decision stage of MCVA approach to increase the classification performance. A well-known appearance based feature descriptor so called Local Binary patterns (LBP) is used to extract the facial features. The performance comparisons are conducted on AR face database with several experiments. It is observed that combining the classifier outputs using decision fusion methods increase the classification performance of MCVA. © Springer Nature Switzerland AG 2020.

关键词： Local binary pattern

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