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Review of Serial and Parallel Min-Cut/Max-Flow algorithms for Computer Vision

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第2期45卷 2310-2329页

作者： Jensen, Patrick M. M. Jeppesen, Niels Dahl, Anders B. B. Dahl, Vedrana A. A. Tech Univ Denmark Dept Appl Math & Comp Sci DK-2800 Lyngby Denmark

Minimum cut/maximum flow (min-cut/max-flow) algorithms solve a variety of problems in computer vision and thus significant effort has been put into developing fast min-cut/max-flow algorithms. As a result, it is difficult to choose an ideal algorithm for a given problem. Furthermore, parallel algorithms have not been thoroughly compared. In this paper, we evaluate the state-of-the-art serial and parallel min-cut/max-flow algorithms on the largest set of computer vision problems yet. We focus on generic algorithms, i.e., for unstructured graphs, but also compare with the specialized GridCut implementation. When applicable, GridCut performs best. Otherwise, the two pseudoflow algorithms, Hochbaum pseudoflow and excesses incremental breadth first search, achieves the overall best performance. The most memory efficient implementation tested is the Boykov-Kolmogorov algorithm. Amongst generic parallel algorithms, we find the bottom-up merging approach by Liu and Sun to be best, but no method is dominant. Of the generic parallel methods, only the parallel preflow push-relabel algorithm is able to efficiently scale with many processors across problem sizes, and no generic parallel method consistently outperforms serial algorithms. Finally, we provide and evaluate strategies for algorithm selection to obtain good expected performance. We make our dataset and implementations publicly available for further research.

关键词： Computer vision Parallel algorithms Benchmark testing Image segmentation Sun partitioning algorithms Heuristic algorithms algorithms computer vision graph algorithms graph-theoretic methods parallel algorithms performance evaluation of algorithms and systems

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Implementation of the waveform relaxation algorithm on a shared memory computer for the transient stability problem - Discussion

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IEEE TRANSACTIONS ON POWER SYSTEMS 1997年第3期12卷 1060-1060页

作者： Crow, ML U.S. Power Inc. Brooklyn Park MN USA School of EECS Washington State University Pullman WA USA

Presents a parallel across space implementation of a Waveform Relaxation (WR) based algorithm. Power system dynamic simulation; Waveform relaxation algorithm; Improving WR convergence and efficiency; Sequent symmetry ... 详细信息

关键词： Power system transients Power system simulation Computational modeling Power system analysis computing partitioning algorithms Power system stability Power system dynamics Concurrent computing Parallel processing Transient analysis

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Efficient Utilization of Multi-Threading Parallelism on Heterogeneous Systems for Sparse Tensor Contraction

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2024年第6期35卷 889-900页

作者： Xiao, Guoqing Yin, Chuanghui Chen, Yuedan Duan, Mingxing Li, Kenli Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Hunan Univ Shenzhen Inst Shenzhen 518063 Peoples R China Cent South Univ Big Data Inst Changsha 410083 Peoples R China

Many fields of scientific simulation, such as chemistry and condensed matter physics, are increasingly eschewing dense tensor contraction in favor of sparse tensor contraction. In this work, we center around binary sparse tensor contraction (SpTC) which has the challenges of index matching and accumulation. To address these difficulties, we present GSpTC, an efficient element-wise SpTC framework on CPU-GPU heterogeneous systems. GSpTC first introduces a fine-grained partitioning strategy based on element-wise tensor contraction. By analyzing and selecting appropriate dimension partitioning strategies, we can efficiently utilize the multi-threading parallelism on GPUs and optimize the overall performance of GSpTC. In particular, GSpTC leverages multi-threading parallelism on GPUs for the contraction phase and merging phase, which greatly accelerates the computation phase in sparse tensor contraction computations. Furthermore, GSpTC employs parallel pipeline technology to hide the data transmission time between the host and the device, further enhancing its performance. As a result, GSpTC achieves an average performance improvement of 267% compared to the previous state-of-the-art framework Sparta.

关键词： Tensors Parallel processing Performance evaluation Graphics processing units Sparse matrices partitioning algorithms Sorting Heterogeneous parallel computing multi-threading parallelism optimization parallel pipeline sparse tensor contraction

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PERFORMANCE ANALYSIS OF A CONVERGED SINGLE-LAYER PERCEPTRON FOR NONSEPARABLE DATA MODELS WITH BIAS TERMS

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1994年第1期42卷 175-188页

作者： BERSHAD, NJ SHYNK, JJ UNIV CALIF SANTA BARBARA DEPT ELECT & COMP ENGNCTR INFORMAT PROC RESSANTA BARBARACA 93106

Rosenblatt's algorithm is a recursive method used to adjust the weights of a single-layer perceptron. It is capable of partitioning the input. signal space into two regions that are separated by a hyperplane boundary. Thus, when the values of the input signal are linearly separable, the algorithm will converge to a stable stationary point that yields zero mean-square error. In this paper, we examine the stationary points of Rosenblatt's algorithm when the data is not linearly separable. A system identification model is used to generate the data. The model incorporates the effects of bias terms so that the hyperplane boundaries do not necessarily pass through the origin of the signal space. An expression, is also derived for the probability of an incorrect classification of the output signal when the weights are converged at a stationary point.

关键词： Performance analysis Data models Convergence partitioning algorithms System identification Vectors Signal processing algorithms Multilayer perceptrons Neural networks Signal generators

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Typical Characteristic-Based Type-2 Fuzzy C-Means Algorithm

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2021年第5期29卷 1173-1187页

作者： Yang, Xiyang Yu, Fusheng Pedrycz, Witold Quanzhou Normal Univ Key Lab Intelligent Comp & Informat Proc Fujian P Quanzhou 362000 Peoples R China Beijing Normal Univ Sch Math Sci Beijing 100875 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6R 2V4 Canada King Abdulaziz Univ Dept Elect & Comp Engn Fac Engn Jeddah 21589 Saudi Arabia Polish Acad Sci Syst Res Inst PL-01447 Warsaw Poland

Type-2 fuzzy sets provide an efficient vehicle for handling uncertainties of real-world problems, including noisy observations. Bringing type-2 fuzzy sets to clustering algorithms offers more flexibility to handle uncertainties associated with membership concepts caused by a noisy environment. However, the existing type-2 fuzzy clustering algorithms suffer from a time-consuming type-reduction process, which not only hampers the clustering performance but also increases the burden of understanding the clustering results. In order to alleviate the problem, this article introduces a set of typical characteristics of type-2 fuzzy sets and establishes a characteristic-based type-2 fuzzy clustering algorithm. Being different from the objective function used in the fuzzy C-means (FCM) algorithm that produces cluster centers and type-1 memberships, the objective function in the proposed algorithm contains additional characteristics of type-2 membership grades, namely, centers of gravity and cardinalities of the secondary fuzzy sets. The derived iterative formulas used for these parameters are much more efficient than the interval type-2 FCM algorithm. The experiments carried out in this study show that the proposed typical characteristic-based type-2 FCM algorithm has an ability of detecting noise as well as assigning suitable membership degrees to the individual data.

关键词： Clustering algorithms Fuzzy sets Noise measurement Phase change materials Uncertainty Linear programming partitioning algorithms Cardinality center of gravity (COG) characteristics fuzzy clustering type-2 fuzzy sets (T2FS)

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DYNAMIC MEASUREMENT OF COMPUTER GENERATED IMAGE SEGMENTATIONS

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1985年第2期7卷 155-164页

作者： LEVINE, MD NAZIF, AM UNIV CAIRO DEPT ELECT ENGNCAIROEGYPT

This paper introduces a general purpose performance measurement scheme for image segmentation algorithms. Performance parameters that function in real-time distinguish this method from previous approaches that depended on an a priori knowledge of the correct segmentation. A low level, context independent definition of segmentation is used to obtain a set of optimization criteria for evaluating performance. Uniformity within each region and contrast between adjacent regions serve as parameters for region analysis. Contrast across lines and connectivity between them represent measures for line analysis. Texture is depicted by the introduction of focus of attention areas as groups of regions and lines. The performance parameters are then measured separately for each area. The usefulness of this approach lies in the ability to adjust the strategy of a system according to the varying characteristics of different areas. This feedback path provides the means for more efficient and error-free processing. Results from areas with dissimilar properties show a diversity in the measurements that is utilized for dynamic strategy setting.","doi":"10.1109/TPAMI.1985.4767640","publicationTitle":"IEEE Transactions on Pattern Analysis and Machine Intelligence","startPage":"155","endPage":"164","rightsLink":"http://***/AppDispatchServlet?publisherName=ieee&publication=0162-8828&title=Dynamic+Measurement+of+Computer+Generated+Image+Segmentations&isbn=&publicationDate=March+1985&author=Martin+D.+Levine&ContentID=10.1109/TPAMI.1985.4767640&orderBeanReset=true&startPage=155&endPage=164&volumeNum=PAMI-7&issueNum=2","displayPublicationTitle":"IEEE Transactions on Pattern Analysis and Machine Intelligence","pdfPath":"/iel5/34/4767634/***","keywords":[{"type":"IEEE Keywords","kwd":["Image generation","Image segmentation","Humans","Area measurement","Feedback","Layout","partitioning algorithms","Focusing","Expert systems","Image analysis"]},{"type":"Author Keywords ","kw

关键词： Image generation Image segmentation Humans Area measurement Feedback Layout partitioning algorithms Focusing Expert systems Image analysis Author Keywords region analysis Edge detection expert system image contrast image segmentation image uniformity partial segmentation

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Research on an Ensemble Classification Algorithm Based on Differential Privacy

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IEEE ACCESS 2020年 8卷 93499-93513页

作者： Jia, Junjie Qiu, Wanyong Northwest Normal Univ Coll Comp Sci & Engn Lanzhou 730070 Peoples R China

In the field of information security, privacy protection based on machine learning is currently a hot topic. Combining differential privacy protection with AdaBoost, a machine learning ensemble classification algorithm, this paper proposes a scheme under differential privacy named CART-DPsAdaBoost (CART-Differential privacy structure of AdaBoost). In the process of boosting, the algorithm combines the idea of bagging, and uses a classification and regression tree (CART) stump as the base learner for ensemble learning. Applying feature perturbation, based on a random subspace algorithm, the exponential mechanism is used to select the splitting point for continuous attributes. We use the Gini index to find the optimal binary partitioning point for discrete attributes and add noise according to the Laplace mechanism. Throughout the process, a privacy budget is allocated in order to meet the appropriate differential privacy protection needs for the current application. Unlike similar algorithms, this method does not require discretization during preprocessing of the data. Experimental results with the Census Income, Digit Recognizer, and Adult Data Set show that while protecting private information, the scheme has little impact on classification accuracy and can effectively address large-scale and high-dimensional data classification problems.

关键词： Classification algorithms Privacy Decision trees Machine learning partitioning algorithms Privacy protection differential privacy machine learning AdaBoost

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Efficient Parallel Processing of All-Pairs Shortest Paths on Multicore and GPU Systems

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第1期70卷 2896-2908页

作者： Alghamdi, Mohammed H. He, Ligang Ren, Shenyuan Maray, Mohammed Univ Jeddah Coll Comp Sci & Engn Dept Informat & Technol Syst Jeddah 23466 Saudi Arabia Univ Warwick Dept Comp Sci Coventry CV4 7AL England Beijing Jiaotong Univ Comp Sci Dept Beijing 100044 Peoples R China Univ Oxford Dept Phys Clarendon Lab Oxford OX1 2JD England King Khalid Univ Coll Comp Sci & Informat Syst Abha 62529 Saudi Arabia

Finding the shortest path between any two nodes in a graph, known as the All-Pairs Shortest Paths (APSP), is a fundamental problem in many data analysis problems, such as supply chains in logistics, routing protocols in IoT networks that involve consumer electronics as well as data analysis for social networking apps and Google Maps apps used by the general public on their smartphones. In this work, we present a novel approach to solve the APSP problem on multicore and GPU systems. In our approach, a graph is first pre-processed by partitioning the graph into sub-graphs. Then, each sub-graph is processed in parallel using any existing shortest path algorithm such as the Floyd-Warshall algorithm or Dijkstra's algorithm. Finally, the distance results in individual sub-graphs are aggregated to obtain the distances of APSP for the entire graph. OpenMP and CUDA are used to implement the parallelization on multicore CPUs and GPUs, respectively. We conduct the extensive experiments with both synthetic and real-world graphs on the JADE (Joint Academic Data Science Endeavour) cluster at the University of Oxford, which is part of the Tier-2 high performance computing facilities in the U.K. In the experiments, we compared our methods with three existing APSP algorithms in the literature, including n-Dijkstra, ParAPSP and SuperFW. The results show that our methods outperform the existing algorithms, achieving the speedup of up to 8.3x over Dijkstra.

关键词： Graphics processing units partitioning algorithms Parallel processing Multicore processing Supply chains Computers Web services All-pairs shortest path graph partition parallel processing supply chain process sheared memory parallelism GPU programming

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An Industrial Dynamic Skyline Based Similarity Joins For Multidimensional Big Data Applications

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2020年第4期16卷 2520-2532页

作者： Yin, Bo Wei, Xuetao Wang, Jin Xiong, Naixue Gu, Ke ChangSha Univ Sci & Technol Sch Comp & Commun Engn Changsha 410114 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China

In the era of data deluge, data analysis has become a key task for many industrial applications, e.g., master data management, and data integration. In particular, similarity join is an important primitive operator to support data analysis, which is to find similar pairs based on similarity functions and thresholds. In this article, we first propose a new similarity join operation called the dynamic skyline join without having to specify any similarity function or similarity threshold, which measures the similarity through multicriteria optimization. The dynamic skyline join operator makes the similarity join more flexible to support different criteria in multidimensional space. However, it is nontrivial to achieve dynamic skyline joins as both join operations and dynamic skyline queries are computationally complex in the increasing volume of real-world data. Therefore, we further propose Grid-SkyJoin, a framework to enable efficient parallel dynamic skyline joins on a shared-nothing cluster. Specifically, we use a grid partitioning to facilitate the data filtering and grouping strategies to provide load balancing and reduce the number of replicas. We also propose a multilevel filtering scheme to prune away a large fraction of unpromising points that do not fit into join results without actual join operations. Extensive experiments using benchmark datasets demonstrate that our filtering scheme can greatly reduce the number of data points to be joined, and our approach is about two times faster compared with the straightforward method in average.

关键词： partitioning algorithms Big Data applications Data integration Filtering Hypercubes Informatics Big data data analysis dynamic skyline similarity join

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Multi-View Clustering With the Cooperation of Visible and Hidden Views

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第2期34卷 803-815页

作者： Deng, Zhaohong Liu, Ruixiu Xu, Peng Choi, Kup-Sze Zhang, Wei Tian, Xiaobin Zhang, Te Liang, Ling Qin, Bin Wang, Shitong Jiangnan Univ Sch Artificial Intelligence & Comp Wuxi Jiangsu Peoples R China Jiangsu Key Lab Media Design & Software Technol Wuxi Jiangsu Peoples R China Hong Kong Polytech Univ Ctr Smart Hlth Sch Nursing Kowloon Hong Kong Peoples R China

Multi-view data are becoming common in real-world applications and many multi-view clustering algorithms have thus been proposed. The existing algorithms usually focus on the cooperation of different visible views in the original space but neglect the influence of the hidden information among these visible views, or they only consider the hidden information among the views. The algorithms are therefore not efficient since the available information is not fully exploited, particularly the otherness information in different views and the consistency information among them. In practice, the otherness and consistency information in multi-view data are both very useful for effective clustering analyses. In this study, a Multi-View clustering algorithm with the Cooperation of Visible and Hidden views, i.e., MV-Co-VH, is proposed. The MV-Co-VH algorithm first projects the multiple views from different visible spaces to the common hidden space by using non-negative matrix factorization to obtain the common hidden view data. Collaborative learning is then implemented in the clustering procedure based on the visible views and the shared hidden view. The experimental results of extensive experiments on UCI multi-view datasets and real-world image multi-view datasets show that the clustering performance of the proposed algorithm is competitive with or even better than that of the existing algorithms.

关键词： Clustering algorithms Clustering methods Matrix decomposition Linear programming Feature extraction partitioning algorithms Data mining Multi-view clustering cooperation of visible and hidden views non-negative matrix factorization

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