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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Wei Li Meng Qin Kai Lei ICNLAB School of Electronics and Computer Engineering (SECE) Peking University Shenzhen China PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Community detection is a fundamental step in social network analysis. In this study, we focus on the hybrid identification of overlapping communities with network topology (e.g., user interaction) and edge-induced content (e.g., user messages). Conventional hybrid methods tend to combine topology and node-induced content to explore disjoint communities, but fail to integrate edge-induced content and to derive overlapping communities. Moreover, although several semantic community detection approaches have been developed, which can generate the semantic description for each community besides the partition result, they can only incorporate the word-level content to derived coarse-grained descriptions. We propose a novel Semantic Link Community Detection (SLCD) model based on non-negative matrix factorization (NMF). It adopts the perspective of link communities to integrate topology and edge-induced content, where the partitioned link communities can be further transformed into an overlapping node-induced membership. Moreover, SLCD incorporates both the word-level and sentence-level content while considering the diversity of user messages. Hence, it can also derive strongly interpretable and multi-view community descriptions simultaneously when community partition is finished. SLCD's superior performance and powerful application ability over other state-of-the-art competitors are further demonstrated in our experiments on a series of real social networks.

关键词： Semantics Network topology Topology Social networking (online) image edge detection Task analysis Cultural differences

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DEEP image CLUSTERING USING CONVOLUTIONAL AUTOENCODER EMBEDDING WITH INCEPTION-LIKE BLOCK

DEEP IMAGE CLUSTERING USING CONVOLUTIONAL AUTOENCODER EMBEDD...

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IEEE International Conference on image processing

作者： Qiang Wang Jiaqing Xu Rongchun Li Peng Qiao Ke Yang Shijie Li Yong Dou National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China School of Computer National University of Defense Technology Changsha China

image clustering is one of the challenging tasks in machine learning, and has been extensively used in various applications. Recently, various deep clustering methods has been proposed. These methods take a two-stage approach, feature learning and clustering, sequentially or jointly. We observe that these works usually focus on the combination of reconstruction loss and clustering loss, relatively little work has focused on improving the learning representation of the neural network for clustering. In this paper, we propose a deep convolutional embedded clustering algorithm with inception-like block (DCECI). Specifically, an inception-like block with different type of convolution filters are introduced in the symmetric deep convolutional network to preserve the local structure of convolution layers. We simultaneously minimize the reconstruction loss of the convolutional autoencoders with inception-like block and the clustering loss. Experimental results on multiple image datasets exhibit the promising performance of our proposed algorithm compared with other competitive methods.

关键词： Convolutional codes Convolution Clustering algorithms image reconstruction Decoding Clustering methods Task analysis

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Red Blood Cells Segmentation: A Fully Convolutional Network Approach

Red Blood Cells Segmentation: A Fully Convolutional Network ...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Ario Sadafi Martin Radolko Iosif Serafeimidis Steffen Hadlak Arivis AG Munich Germany

ISBN: (纸本)9781728111421;9781728111414

Red blood cell segmentation in microscopic images is the first step for various clinical studies carried out on blood samples such as cell counting, cell shape identification, etc. Conventional methods while often showing a high accuracy are heavily depending on the acquisition modality. Deep learning approaches have shown to be more robust regarding such modalities and still showing a comparable accuracy. In this paper, we first investigate necessary steps to apply a specific type of deep learning methods, namely fully convolutional networks, to red blood cell segmentation. Based on data given and constraints imposed by our partners mainly regarding a high throughput of their data we then describe an exemplary application. First results show, that even with a focus on high performance a good accuracy above 90% can be reached.

关键词： image segmentation Training Shape Deep learning Red blood cells image analysis

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image Deno sing via Multiscale Nonlinear Diffusion Models

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SIAM JOURNAL ON IMAGING SCIENCES 2017年第3期10卷 1234-1257页

作者： Feng, Wensen Qiao, Peng Xi, Xuanyang Chen, Yunjin Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Natl Univ Def Technol Sch Comp Natl Lab Parallel & Distributed Proc Changsha 410073 Hunan Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China ULSee Inc Hangzhou 310016 Zhejiang Peoples R China

image denoising is a fundamental operation in image processing and holds considerable practical importance for various real-world applications. Arguably several thousands of papers are dedicated to image denoising. In the past decade, state-of-the-art denoising algorithms have been clearly dominated by nonlocal patch-based methods, which explicitly exploit patch self-similarity within the targeted image. However, in the past two years, discriminatively trained local approaches have started to outperform previous nonlocal models and have been attracting increasing attention due to the additional advantage of computational efficiency. Successful approaches include cascade of shrinkage fields (CSF) and trainable nonlinear reaction diffusion (TNRD). These two methods are built on the filter response of linear filters of small size using feed forward architectures. Due to the locality inherent in local approaches, the CSF and TNRD models become less effective when the noise level is high and consequently introduce some noise artifacts. In order to overcome this problem, in this paper we introduce a multiscale strategy. To be specific, we build on our newly developed TNRD model, adopting the multiscale pyramid image representation to devise a multiscale nonlinear diffusion process. As expected, all the parameters in the proposed multiscale diffusion model, including the filters and the influence functions across scales, are learned from training data through a loss-based approach. Numerical results on Gaussian and Poisson denoising substantiate that the exploited multiscale strategy can successfully boost the performance of the original TNRD model with a single scale. As a consequence, the resulting multiscale diffusion models can significantly suppress the typical incorrect features for those noisy images with heavy noise. It turns out that multiscale TNRD variants achieve better performance than state-of-the-art denoising methods.

关键词： image denoising multiscale pyramid image representation trainable nonlinear reaction diffusion model Gaussian denoising Poisson denoising

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On-Demand processing for Remote Sensing Big Data Analysis 15

On-Demand Processing for Remote Sensing Big Data Analysis

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15th IEEE International Symposium on parallel and distributed processing with Applications (ISPA) / 16th IEEE International Conference on Ubiquitous Computing and Communications (IUCC)

作者： Huang, Zhenchun Zhong, Anrun Li, Guoqing Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Chinese Acad Sci Inst Remote Sensing & Digital Earth Beijing Peoples R China

ISBN: (纸本)9781538637906

In the recent decades, remote sensing data are rapidly growing in size and variety, and considered as "big geo data" because of their huge data volume, significant heterogeneity and challenge of fast analysis. In the traditional remote sensing analysis workflows, the data transfer for downloading raw image files to local workstations often costs a lot of time and slows down the data analysis workflows. Because results of remote sensing data analysis models are usually much smaller than raw data to be processed, "on-demand processing", which tries to upload data analysis models and execute them "near" where data stores, can significantly accelerate the execution of remote sensing analysis workflows. In this paper, a framework for on-demand remote sensing data analysis is proposed based on three-layered architecture;XML/JSON based runtime environment description;and on-demand model deployment methods. The evaluation on a prototype system shows that on-demand processing framework accelerates the execution of analysis models in 2.8 similar to 12.7 times by reducing data transfers, especially for those analysis workflows which transfer data through low bandwidth Internet. By on-demand processing, classical remote sensing data service systems can evolve into remote sensing data processing infrastructures, which provide IaaS (Infrastructure-as-a-Service) and PaaS (Platform-as-a Service) services, and make it possible to exchange knowledge among scientists by sharing models. Furthermore, a remote sensing data analysis platform for carbon satellites is designed based on the on-demand processing proposed by this paper and will soon be implemented under the support of SunWay-TaihuLight, the world's most powerful super computer.

关键词： remote sensing data analysis on-demand processing big geo data

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parallelization of Synthetic Aperture Radar (SAR) image Formation Algorithm 1st

Parallelization of Synthetic Aperture Radar (SAR) Image Form...

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1st International Conference on Computational Intelligence and Informatics (ICCII)

作者： Rao, P. V. R. R. Bhogendra Shashank, S. S. Def Res & Dev Lab Hyderabad Andhra Pradesh India BrahMos Aerosp Private Ltd Hyderabad Andhra Pradesh India

ISBN: (纸本)9789811024719;9789811024702

Synthetic aperture radar (SAR)-based platforms have to process increasingly large number of complex floating-point operations and have to meet hard real-time deadlines. However, real-time use of SAR is severely restricted by computation time taken for image formation. One of the classical methods of reducing this computation time to make it suitable for real-time application is multi-processing. A successful attempt has been made by the authors to develop and test a parallel algorithm for synthetic aperture radar image formation, and the results are presented in this paper.

关键词： Synthetic aperture radar SAR image formation Digital signal processing parallel computing distributed computing

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A Novel Approach for Identifying Lateral Movement Attacks Based on Network Embedding

A Novel Approach for Identifying Lateral Movement Attacks Ba...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Mingyi Chen Yepeng Yao Junrong Liu Bo Jiang Liya Su Zhigang Lu School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728111421;9781728111414

The growing targeted incidents constitute a permanent menace to internal security with more frequent data breach and service interruption events nowadays. Attackers move laterally and reside in the internal systems for accessing valuable information continuously. This paper proposed a novel approach based on network embedding synthesizing the information on hosts, traffics and correlations to forestall further loss under a deliberate attack. The approach begins with constructing a host communication graph with features extracted from the original data recorded in the internal network and the topology structures of the constructed network. Inspired by previous works, the approach uses a features aggregation learning method, that is composing the features on vertices and edges with neighbors' features to aggregate new composite features. Then the features are selected and learned iteratively. The ultimately selected features were reduced to lower dimension for training and used for the malicious host classification task. Besides, the approach can reemployment the classification results to optimize the dimensionality reduction. Compared with the state-of-the-art models and methods, the proposed approach is (i) flexible with multiple types of data and exchangeable methods, (ii) accurate in feature learning, selecting and extracting with the remarkably average accuracy of 99.9% and the average precision of 91.3%.

关键词： Feature extraction Topology Training Biological system modeling image edge detection Security Data mining

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Study about the Edge Detection Algorithm and its Applications 18

Study about the Edge Detection Algorithm and its Application...

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18th International Carpathian Control Conference (ICCC)

作者： Popa, Bogdan Univ Craiova Dept Automat & Appl Informat Craiova Romania

ISBN: (纸本)9781509048625

Nowadays one of the most used algorithm for the image processing systems is the edge detection algorithm, starting from the identification systems in different domains to the complex systems direct from industry. With more different approaches this algorithm is today necessary in priority areas and his performance of time processing, quality differ in each case. In the backside of the algorithm there are a lot of mathematical calculus that influence the speed and the accuracy of the results. This article highlights some methods that compose this algorithm and propose some ideas and some tests over the possibility of distributed calculus that influences his performance. In this study it is proposed a set of analysis over the results over more tests and case scenarios that show them main problems in the calculus part. Also a parallel implementation that can offer a better perspective of this algorithm is proposed. Starting with this point over the edge detection of the algorithm, in this paper it is revealed also a good utilization in another different domain for this algorithm.

关键词： image processing edge detection algorithm performance case study

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Cloud masking and removal in remote sensing image time series

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JOURNAL OF APPLIED REMOTE SENSING 2017年第1期11卷

作者： Gomez-Chova, Luis Amoros-Lopez, Julia Mateo-Garcia, Gonzalo Munoz-Mari, Jordi Camps-Valls, Gustau Univ Valencia Image Proc Lab Catedratico Jose Beltran 2 E-46980 Valencia Spain

Automatic cloud masking of Earth observation images is one of the first required steps in optical remote sensing data processing since the operational use and product generation from satellite image time series might be hampered by undetected clouds. The high temporal revisit of current and forthcoming missions and the scarcity of labeled data force us to cast cloud screening as an unsupervised change detection problem in the temporal domain. We introduce a cloud screening method based on detecting abrupt changes along the time dimension. The main assumption is that image time series follow smooth variations over land (background) and abrupt changes will be mainly due to the presence of clouds. The method estimates the background surface changes using the information in the time series. In particular, we propose linear and nonlinear least squares regression algorithms that minimize both the prediction and the estimation error simultaneously. Then, significant differences in the image of interest with respect to the estimated background are identified as clouds. The use of kernel methods allows the generalization of the algorithm to account for higher-order (nonlinear) feature relations. After the proposed cloud masking and cloud removal, cloud-free time series at high spatial resolution can be used to obtain a better monitoring of land cover dynamics and to generate more elaborated products. The method is tested in a dataset with 5-day revisit time series from SPOT-4 at high resolution and with Landsat-8 time series. Experimental results show that the proposed method yields more accurate cloud masks when confronted with state-of-the-art approaches typically used in operational settings. In addition, the algorithm has been implemented in the Google Earth Engine platform, which allows us to access the full Landsat-8 catalog and work in a parallel distributed platform to extend its applicability to a global planetary scale. (C) 2017 Society of Photo-Optical Instrumenta

关键词： image time series multitemporal analysis change detection cloud masking kernel ridge regression Google Earth Engine

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image Annotation by Deep Neural Networks with Attention Shaping 9

Image Annotation by Deep Neural Networks with Attention Shap...

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9th International Conference on Digital image processing (ICDIP)

作者： Zheng, Kexin Lv, Shaohe Ma, Fang Chen, Fei Jin, Chi Dou, Yong Natl Univ Def Technol Natl Lab Parallel & Distributed Proc 109 Deya Rd Changsha 410073 Hunan Peoples R China Univ South China Sch Comp Sci & Technol Changsha 410012 Hunan Peoples R China

ISBN: (纸本)9781510613058;9781510613041

image annotation is a task of assigning semantic labels to an image. Recently, deep neural networks with visual attention have been utilized successfully in many computer vision tasks. In this paper, we show that conventional attention mechanism is easily misled by the salient class, i.e., the attended region always contains part of the image area describing the content of salient class at different attention iterations. To this end, we propose a novel attention shaping mechanism, which aims to maximize the non-overlapping area between consecutive attention processes by taking into account the history of previous attention vectors. Several weighting polices are studied to utilize the history information in different manners. In two benchmark datasets, i.e., PASCAL VOC2012 and MIRFlickr-25k, the average precision is improved by up to 10% in comparison with the state-of-the-art annotation methods.

关键词： computer vision image annotation DNN attention mechanism

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