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arXiv 2018年

作者： Li, Shan Deng, Weihong Pattern Recognition and Intelligent System Laboratory School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China

With the transition of facial expression recognition (FER) from laboratory-controlled to challenging in-the-wild conditions and the recent success of deep learning techniques in various fields, deep neural networks have increasingly been leveraged to learn discriminative representations for automatic FER. Recent deep FER systems generally focus on two important issues: overfitting caused by a lack of sufficient training data and expression-unrelated variations, such as illumination, head pose and identity bias. In this paper, we provide a comprehensive survey on deep FER, including datasets and algorithms that provide insights into these intrinsic problems. First, we introduce the available datasets that are widely used in the literature and provide accepted data selection and evaluation principles for these datasets. We then describe the standard pipeline of a deep FER system with the related background knowledge and suggestions of applicable implementations for each stage. For the state of the art in deep FER, we review existing novel deep neural networks and related training strategies that are designed for FER based on both static images and dynamic image sequences, and discuss their advantages and limitations. Competitive performances on widely used benchmarks are also summarized in this section. We then extend our survey to additional related issues and application scenarios. Finally, we review the remaining challenges and corresponding opportunities in this field as well as future directions for the design of robust deep FER systems. Copyright © 2018, The Authors. All rights reserved.

关键词： Surveys

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Comparison of Backbones for Semantic Segmentation Network

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Journal of Physics: Conference Series 2020年第1期1544卷

作者： Rongyu Zhang Lixuan Du Qi Xiao Jiaming Liu International School Beijing University of Post and Telecommunication BEIJING 100876 CHINA International School of business&Fiance Sun Yat-Sen University GUANGZHOU 510275 CHINA Pattern Recognition and Intelligent System Lab Beijing University of Posts and Telecommunications BEIJING 100876 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.

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Camera Abnormal Movement and Foreign Object Invasion Detection Based on Cumulative Edge Distribution Probability Model

Camera Abnormal Movement and Foreign Object Invasion Detecti...

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2018 International Conference on Security, pattern Analysis, and Cybernetics, SPAC 2018

作者： Yu, Xiangru Cai, Fudong Dou, Yimin Li, Jinping Institute of Pattern Recognition and Intelligent System School of Information Science and Engineering University of Jinan Jinan China Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan China Shandong College University Key Laboratory of Information Processing and Cognitive Computing in 13th Five-year University of Jinan Jinan China Shandong Senter Electronic Co. Ltd Zibo China

ISBN: (纸本)9781728105512

In practice, there often exist some occasions where video surveillance can only be realized by using rechargeable batteries due to the high cost of power supply. In order to extend the battery life, images can only be taken at a certain interval. Obviously, image difference is an important method to obtain various changes in this case. Among these changes, foreign invasion is one of the focuses. However, due to bolt shedding, abnormal weather and other factors, the camera may have abnormal movement which may seriously affect the detection effect of image difference method. Therefore, it is an urgent issue to find an effective way to detect the abnormal movement of camera and improve the detection accuracy of foreign invasion. Considering the characteristics of this kind of image sequences, we propose an effective algorithm to detect the camera abnormal movement and foreign object invasion based on a cumulative edge distribution probability model. Since sky region is relatively simple, we only discuss changes in sky region to detect camera abnormal movement. Our algorithm has 6 basic steps: firstly, segment the sky region;secondly, extract the edge information of the current image and the preceding adjacent image in the image sequence;thirdly, determine if the edge information of two adjacent images coincide. If consistent, then go to the next step, otherwise, it indicates that the camera has abnormal movement, then alarm;fourthly, calculate the cumulative edge probability distribution model in the sky region by using the historical image sequence;fifthly, by using adaptive Parzen window, determine if foreign object invasion exists by comparing the probability model of cumulative edge distribution with edge distribution of the current image;sixthly, update the cumulative edge distribution probability model in sky region. The algorithm achieves good results in practical applications. Through the test of thousands of images taken in the wild, the detection accuracy of

关键词： Cameras

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Camera Abnormal Movement and Foreign Object Invasion Detection Based on Cumulative Edge Distribution Probability Model

Camera Abnormal Movement and Foreign Object Invasion Detecti...

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International Conference on Security, pattern Analysis, and Cybernetics (SPAC)

作者： Xiangru Yu Fudong Cai Yimin Dou Jinping Li Institute of Pattern Recognition and Intelligent System School of Information Science and Engineering University of Jinan Jinan China Shandong Senter Electronic Co. Ltd Zibo China

ISBN: (纸本)9781728105529

In practice, there often exist some occasions where video surveillance can only be realized by using rechargeable batteries due to the high cost of power supply. In order to extend the battery life, images can only be taken at a certain interval. Obviously, image difference is an important method to obtain various changes in this case. Among these changes, foreign invasion is one of the focuses. However, due to bolt shedding, abnormal weather and other factors, the camera may have abnormal movement which may seriously affect the detection effect of image difference method. Therefore, it is an urgent issue to find an effective way to detect the abnormal movement of camera and improve the detection accuracy of foreign invasion. Considering the characteristics of this kind of image sequences, we propose an effective algorithm to detect the camera abnormal movement and foreign object invasion based on a cumulative edge distribution probability model. Since sky region is relatively simple, we only discuss changes in sky region to detect camera abnormal movement. Our algorithm has 6 basic steps: firstly, segment the sky region; secondly, extract the edge information of the current image and the preceding adjacent image in the image sequence; thirdly, determine if the edge information of two adjacent images coincide. If consistent, then go to the next step, otherwise, it indicates that the camera has abnormal movement, then alarm; fourthly, calculate the cumulative edge probability distribution model in the sky region by using the historical image sequence; fifthly, by using adaptive Parzen window, determine if foreign object invasion exists by comparing the probability model of cumulative edge distribution with edge distribution of the current image; sixthly, update the cumulative edge distribution probability model in sky region. The algorithm achieves good results in practical applications. Through the test of thousands of images taken in the wild, the detection accurac

关键词： Image edge detection Image sequences Image segmentation Cameras Batteries Meteorology Adaptation models

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Semi-heterogeneous three-way joint embedding network for sketch-based image retrieval

arXiv

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arXiv 2019年

作者： Lei, Jianjun Song, Yuxin Peng, Bo Ma, Zhanyu Shao, Ling Song, Yi-Zhe School of Electrical and Information Engineering Tianjin University Tianjin300072 China Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing100876 China Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates SketchX Lab Centre for Vision Speech and Signal Processing University of Surrey Surrey GuildfordGU2 7XH United Kingdom

Sketch-based image retrieval (SBIR) is a challenging task due to the large cross-domain gap between sketches and natural images. How to align abstract sketches and natural images into a common high-level semantic space remains a key problem in SBIR. In this paper, we propose a novel semi-heterogeneous three-way joint embedding network (Semi3-Net), which integrates three branches (a sketch branch, a natural image branch, and an edgemap branch) to learn more discriminative cross-domain feature representations for the SBIR task. The key insight lies with how we cultivate the mutual and subtle relationships amongst the sketches, natural images, and edgemaps. A semi-heterogeneous feature mapping is designed to extract bottom features from each domain, where the sketch and edgemap branches are shared while the natural image branch is heterogeneous to the other branches. In addition, a joint semantic embedding is introduced to embed the features from different domains into a common high-level semantic space, where all of the three branches are shared. To further capture informative features common to both natural images and the corresponding edgemaps, a co-attention model is introduced to conduct common channel-wise feature recalibration between different domains. A hybrid-loss mechanism is designed to align the three branches, where an alignment loss and a sketch-edgemap contrastive loss are presented to encourage the network to learn invariant cross-domain representations. Experimental results on two widely used category-level datasets (Sketchy and TU-Berlin Extension) demonstrate that the proposed method outperforms state-of-the-art methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantics

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Statistical speech model description with VMF mixture model

arXiv

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arXiv 2018年

作者： Ma, Zhanyu Leijon, Arne Pattern Recognition and Intelligent System Lab Beijing University of Posts and Telecommunications Beijing100876 China School of Electrical Engineering KTH - Royal Institute of Technology StockholmSE-100 44 Sweden

Efficient quantization of the linear predictive coding (LPC) parameters plays a key role in parametric speech coding. The line spectral frequency (LSF) representation of the LPC parameters has found its applications in speech model quantization. In practical implementation of vector quantization (VQ), probability density function (PDF)-optimized VQ has been shown to be more efficient than the VQ based on training data. In this paper, we present the LSF parameters by a unit vector form, which has directional characteristics. The underlying distribution of this unit vector variable is modeled by a von Mises-Fisher mixture model (VMM). With the high rate theory, the optimal inter-component bit allocation strategy is proposed and the distortion-rate (D-R) relation is derived for the VMM based-VQ (VVQ). Experimental results show that the VVQ outperforms our recently introduced DVQ and the conventional GVQ. Copyright © 2018, The Authors. All rights reserved.

关键词： Vector quantization

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Progressive Refinement Bilateral Filter

Progressive Refinement Bilateral Filter

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IEEE International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Chanyi Lu Yong Zhao Lin Wang Guiying Zhang Fujian Feng Li Zhang College of Data Science and Information Engineering Guizhou Minzu University Guiyang China School of Electronic and Computer Engineering Shenzhen Graduate School of Peking University Shenzhen China Guizhou Key Laboratory of Pattern Recognition and Intelligent System Guizhou Minzu University Guiyang China Department of Medical Information Engineering Zunyi Medical University Zunyi China

ISBN: (纸本)9781538678626;9781538678619

In this paper, a coarse-to-fine framework for image noise removal is proposed. The bilateral filter is redefined by the manner of progressive refining to effectively eliminate noise, thus forming progressive refinement bilateral filter (PRBF), which estimates the pixel values of a noisy image through the method of progressive refinement with gradual recursion and its result retains more details, infinite close to the original image. PRBF is further integrated into the denoising model for the details processing again after denoising of the major part of the image. Our algorithm is evaluated and compared on two large standardized datasets and a number of selected images. The experimental results show that our algorithm consistently outperforms other approaches for image denoising and demonstrates the effectiveness of our framework for stereo matching.

关键词： Noise reduction Image denoising Mathematical model Filtering Filtering algorithms Kernel Biomedical imaging

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SOUND CLASSIFICATION BASED ON SPECTROGRAM FOR SURVEILLANCE APPLICATIONS 5

SOUND CLASSIFICATION BASED ON SPECTROGRAM FOR SURVEILLANCE A...

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2016 5th IEEE International Conference on Network Infrastructure and Digital Content(IC-NIDC 2016)

作者： Yingjie Li Gang Liu Pattern Recognition and Intelligent System Laboratory School of Information and Communication EngineeringBeijing University of Posts and Telecommunications

ISBN: (纸本)9781509012473

This paper presents an audio event classification algorithm which automatically classifies an audio event as footstep,glass breaking,gunshot or scream mainly for surveillance ***,the Gabor feature of the audio spectrogram is extracted,there are two kinds of Gabor features,namely global Gabor feature and local Gabor *** we use Principal Components Analysis(PCA) and Linear Discriminant Analysis(LDA) to compress the feature dimension,finally the K nearest neighbor classifier(KNN) is used to recognize audio *** carried out extensive experiments on the clean and noisy audio *** results demonstrate that the algorithm is able to guarantee a recall of 96.1%on clean sets and is proved to be more effective than traditional methods.

关键词： Spectrogram Global Gabor feature Local Gabor feature PCA LDA

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HUMMING FEATURE EXTRACTION BASED ON LOCALITY STATISTICAL ANALYSIS 5

HUMMING FEATURE EXTRACTION BASED ON LOCALITY STATISTICAL ANA...

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2016 5th IEEE International Conference on Network Infrastructure and Digital Content(IC-NIDC 2016)

作者： Gang Liu Yanyao Bian Yanqiu Wang Pattern Recognition and Intelligent System Laboratory School of Information and Communication EngineeringBeijing University of Posts and Telecommunications

ISBN: (纸本)9781509012473

This paper presents a novel humming feature extraction algorithm based on locality statistical analysis to tackle the problem of the instability of humming features in the query by humming（QBH） *** carrying out statistics to humming notes sequences in both longitudinal vocal range distribution and horizontal temporal variation distribution,we can obtain the locality statistical humming *** we concatenate several features using the idea of N-gram to improve feature *** the framework of QBH based on Locality Sensitive Hashing（LSH）,the proposed method has achieves 86%top-1 rate and 92%top-5 rate in the experiment,indicating the effectiveness of the method.

关键词： query by humming feature extraction locality statistics note distribution temporal distribution

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CROSS-MODAL SUBSPACE LEARNING FOR SKETCH-BASED IMAGE RETRIEVAL:A COMPARATIVE STUDY 5

CROSS-MODAL SUBSPACE LEARNING FOR SKETCH-BASED IMAGE RETRIEV...

引用

2016 5th IEEE International Conference on Network Infrastructure and Digital Content(IC-NIDC 2016)

作者： Peng Xu Ke Li Zhanyu Ma Yi-Zhe Song Liang Wang Jun Guo Pattern Recognition and Intelligent System Lab. Beijing University of Posts and Telecommunications SketchX Lab School of Electronic Engineering and Computer ScienceQueen Mary University of London National Lab of Pattern Recognition Institute of AutomationChinese Academy of Sciences

ISBN: (纸本)9781509012473

Sketch-based image retrieval（SBIR）has become a prominent research topic in recent years due to the proliferation of touch *** problem is however very challenging for that photos and sketches are inherently modeled in different *** are accurate（colored and textured）depictions of the real-world,whereas sketches are highly abstract（black and white）renderings often drawn from human *** naturally motivates us to study the effectiveness of various cross-modal retrieval methods in ***,to the best of our knowledge,all established cross-modal algorithms are designed to traverse the more conventional cross-modal gap of image and text,making their general applicableness to SBIR *** this paper,we design a series of experiments to clearly illustrate circumstances under which cross-modal methods can be best utilized to solve the SBIR *** specifically,we choose six state-of-the-art cross-modal subspace learning approaches that were shown to work well on image-text and conduct extensive experiments on a recently released SBIR ***,we present detailed comparative analysis of the experimental results and offer insights to benefit future research.

关键词： SBIR Cross-modal Subspace learning Comparison

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