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Key-frame selection for automatic summarization of surveillance videos: a method of multiple change-point detection

MACHINE VISION AND APPLICATIONS 2018年第7期29卷 1101-1117页

作者： Gao, Zhen Lu, Guoliang Lyu, Chen Yan, Peng Shandong Univ Sch Mech Engn Natl Demonstrat Ctr Expt Mech Engn Educ Key Lab High Efficiency & Clean Mech Mfg MOE Jinan Shandong Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan Shandong Peoples R China

Recent years have witnessed a drastic growth of various videos in real-life scenarios, and thus there is an increasing demand for a quick view of such videos in a constrained amount of time. In this paper, we focus on automatic summarization of surveillance videos and present a new key-frame selection method for this task. We first introduce a dissimilarity measure based on f-divergence by a symmetric strategy for multiple change-point detection and then use it to segment a given video sequence into a set of non-overlapping clips. Key frames are extracted from the resulting video clips by a typical clustering procedure for final video summary. Through experiments on a wide range of testing data, excellent performances, outperforming given state-of-the-art competitors, have been demonstrated which suggests good potentials of the proposed method in real-world applications.

关键词： Surveillance video summarization Key-frame selection multiple change-point detection

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A novel group VIF regression for group variable selection with application to multiple change-point detection

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JOURNAL OF APPLIED STATISTICS 2023年第2期50卷 247-263页

作者： Ding, Hao Zhang, Yan Wu, Yuehua York Univ Dept Math & Stat Toronto ON Canada

In this paper, we propose a novel group variance inflation factor (VIF) regression model for tackling large data sets where data follows a grouped structure. Unlike classical penalized methods, this approach can perform group variable selection in a sparse model, which is quite different from the classical penalized methods. We further adapt the proposed method associated with a two-stage procedure for detecting multiple change-point in linear models. We carry out extensive simulation studies to show that the proposed group variable selection and change-point detection methods are stable and efficient. Finally, we provide two real data examples, including a body fat data set and an air pollution data set, to illustrate the performance of our algorithms in group selection and change-point detection.

关键词： Group variable selection multiple change-point detection stagewise regression VIF regression

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Genetic algorithm with a Bayesian approach for multiple change-point detection in time series of counting exceedances for specific thresholds

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JOURNAL OF THE KOREAN STATISTICAL SOCIETY 2023年第4期52卷 982-1024页

作者： Suarez-Sierra, Biviana Marcela Coen, Arrigo Taimal, Carlos Alberto EAFIT Univ Comp & Analyt Area Sch Appl Sci & Engn Carrera 49Cl 7 Sir 50 Medellin 050022 Antioquia Colombia Univ Nacl Autonoma Mexico Fac Sci Dept Math Mexico City 10587 Mexico

Although the applications of Non-Homogeneous Poisson Processes (NHPP) to model and study the threshold overshoots of interest in different time series of measurements have proven to provide good results, they needed to be complemented with an efficient and automatic diagnostic technique to establish the location of the change-points, which, when taken into account, make the estimated model fit poorly in regards of the information contained in the real one. Because of this, a new method is proposed to solve the segmentation uncertainty of the time series of measurements, where the generating distribution of exceedances of a specific threshold is the focus of investigation. One of the great contributions of the present algorithm is that all the days that trespassed are candidates to be a change-point, so all the possible configurations of overflow days under the heuristics of a genetic algorithm are the possible chromosomes, which will unite to produce new solutions. Also, such methods will be guarantee to non-local and the best possible one solution, reducing wasted machine time evaluating the least likely chromosomes to be a feasible solution. The analytical evaluation technique will be by means of the Minimum Description Length (MDL) as the objective function, which is the joint posterior distribution function of the parameters of the NHPP of each regime and the change-points that determines them and which account as well for the influence of the presence of said times. Thus, one of the practical implications of the present work comes in terms of overcoming the need of modeling the time series of measurements, where the distributions of exceedances of certain thresholds, or where the counting of certain events involving abrupt changes, is the main focus with applications in phenomena such as climate change, information security and epidemiology, to name a few.

关键词： multiple change-point detection Genetic Algorithms Minimum Description Length Non-homogeneous Poisson Processes Maximum A Posteriori Estimation

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Consistent two-stage multiple change-point detection in linear models

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CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE 2016年第2期44卷 161-179页

作者： Jin, Baisuo Wu, Yuehua Shi, Xiaoping Univ Sci & Technol China Dept Stat & Finance 96 Jinzhai Rd Hefei 230026 Anhui Peoples R China York Univ Dept Math & Stat 4700 Keele St Toronto ON M3J 1P3 Canada Thompson Rivers Univ Dept Math & Stat 900 McGill Rd Kamloops BC V2C 0C8 Canada

A two-stage procedure for simultaneously detecting multiple change-points in linear models is developed. In the cutting stage, the change-point problem is converted into a model selection problem so that a modern model selection method can be applied. In the refining stage, the change-points obtained in the cutting stage are finalized via a refining method. Under mild conditions, consistency of the number of change-point estimates is established. The new procedure is fast and accurate, as shown in simulation studies. Its applicability in real situations is demonstrated via well-log and ozone data. (C) 2016 Statistical Society of Canada

关键词： Adaptive lasso consistency MCP/SCAD model selection multiple change-point detection

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BAYESIAN multiple change-point detection WITH LIMITED COMMUNICATION

BAYESIAN MULTIPLE CHANGE-POINT DETECTION WITH LIMITED COMMUN...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Halme, Topi Nitzan, Eyal Poor, H. Vincent Koivunen, Visa Aalto Univ Espoo Finland Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9781509066315

Several modern applications involve large-scale sensor networks for statistical inference. For example, such sensor networks are of significant interest for Internet of Things applications. In this paper, we consider Bayesian multiple change-point detection using a sensor network in which a fusion center can receive a data stream from each sensor. Due to communication limitations, the fusion center monitors only a subset of the data streams at each time slot. We propose a detection procedure that handles these limitations by monitoring the sensors with the highest posterior probabilities of change points having occurred. It is shown that the proposed procedure attains an average detection delay that does not increase with the number of sensors, while controlling the false discovery rate. The proposed procedure is also shown to be useful for unveiling the tradeoff between reducing the average detection delay and reducing the average number of observations drawn until discovery.

关键词： Sensor networks multiple change-point detection communication limitations average detection delay false discovery rate

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Robust Multi-Task Regression with Shifting Low-Rank Patterns

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Acta Mathematica Sinica,English Series 2025年第2期41卷 677-702页

作者： Junfeng Cui Guanghui Wang Fengyi Song Xiaoyan Ma Changliang Zou School of Mathematical Sciences Shenzhen UniversityShenzhen 518060P.R.China NITFID School of Statistics and Data ScienceLPMC and KLMDASR and LEBPSNankai UniversityTianjin 300071P.R.China School of Mathematics and Statistics Ningxia UniversityYinchuan 750021P.R.China

We consider the problem of multi-task regression with time-varying low-rank patterns,where the collected data may be contaminated by heavy-tailed distributions and/or *** approach is based on a piecewise robust multi-task learning formulation,in which a robust loss function—not necessarily to be convex,but with a bounded derivative—is used,and each piecewise low-rank pattern is induced by a nuclear norm regularization *** propose using the composite gradient descent algorithm to obtain stationary points within a data segment and employing the dynamic programming algorithm to determine the optimal *** theoretical properties of the detected number and time points of pattern shifts are studied under mild *** results confirm the effectiveness of our method.

关键词： Low-rank matrix estimation multiple change-point detection multi-task regression robust learning

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change-point detection IN MULTINOMIAL DATA WITH A LARGE NUMBER OF CATEGORIES

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ANNALS OF STATISTICS 2018年第5期46卷 2020-2044页

作者： Wang, Guanghui Zou, Changliang Yin, Guosheng Nankai Univ Inst Stat Tianjin Peoples R China Nankai Univ LPMC Tianjin Peoples R China Univ Hong Kong Dept Stat & Actuarial Sci Hong Kong Hong Kong Peoples R China

We consider a sequence of multinomial data for which the probabilities associated with the categories are subject to abrupt changes of unknown magnitudes at unknown locations. When the number of categories is comparable to or even larger than the number of subjects allocated to these categories, conventional methods such as the classical Pearson's chi-squared test and the deviance test may not work well. Motivated by high-dimensional homogeneity tests, we propose a novel change-point detection procedure that allows the number of categories to tend to infinity. The null distribution of our test statistic is asymptotically normal and the test performs well with finite samples. The number of change-points is determined by minimizing a penalized objective function based on segmentation, and the locations of the change-points are estimated by minimizing the objective function with the dynamic programming algorithm. Under some mild conditions, the consistency of the estimators of multiple change-points is established. Simulation studies show that the proposed method performs satisfactorily for identifying change-points in terms of power and estimation accuracy, and it is illustrated with an analysis of a real data set.

关键词： Asymptotic normality categorical data high-dimensional homogeneity test multiple change-point detection sparse contingency table

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Bayesian Quickest detection in Sensor Arrays

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SEQUENTIAL ANALYSIS-DESIGN METHODS AND APPLICATIONS 2012年第4期31卷 481-504页

作者： Ludkovski, Michael Univ Calif Santa Barbara Dept Stat & Appl Probabil Santa Barbara CA 93106 USA

We study Bayesian quickest detection problems with sensor arrays. An underlying signal is assumed to gradually propagate through a network of several sensors, triggering a cascade of interdependent change-points. The aim of the decision maker is to centrally fuse all available information to find an optimal detection rule that minimizes Bayes risk. We develop a tractable continuous-time formulation of this problem focusing on the case of sensors collecting point process observations and monitoring the resulting changes in intensity and type of observed events. Our approach uses methods of nonlinear filtering and optimal stopping and lends itself to an efficient numerical scheme that combines particle filtering with a Monte Carlo-based approach to dynamic programming. The developed models and algorithms are illustrated with plenty of numerical examples.

关键词： Bayesian quickest detection multiple change-point detection Particle filtering Sensor arrays

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Ensemble binary segmentation for irregularly spaced data with change-points

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JOURNAL OF THE KOREAN STATISTICAL SOCIETY 2022年第1期51卷 65-86页

作者： Korkas, Karolos K. Univ Greenwich Sch Comp & Math Sci London England

We propose a new technique for consistent estimation of the number and locations of the change-points in the structure of an irregularly spaced time series. The core of the segmentation procedure is the ensemble binary segmentation method (EBS), a technique in which a large number of multiple change-point detection tasks using the binary segmentation method are applied on sub-samples of the data of differing lengths, and then the results are combined to create an overall answer. We do not restrict the total number of change-points a time series can have, therefore, our proposed method works well when the spacings between change-points are short. Our main change-point detection statistic is the time-varying autoregressive conditional duration model on which we apply a transformation process in order to decorrelate it. To examine the performance of EBS we provide a simulation study for various types of scenarios. A proof of consistency is also provided. Our methodology is implemented in the R package eNchange, available to download from CRAN.

关键词： multiple change-point detection Conditional duration models Binary segmentation Ensemble methods Non-stationarity

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Analyzing growth components in trees

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JOURNAL OF THEORETICAL BIOLOGY 2007年第3期248卷 418-447页

作者： Guedon, Yann Caraglio, Yves Heuret, Patrick Lebarbier, Emilie Meredieu, Celine INRA CIRAD UMAR DAP Montpellier 5 France CIRAD UMAR DAP F-34398 Montpellier 5 France Univ Montpellier 2 INRA CNRS CIRAD UMAR IRDBotan & Bioinformat Architecture F-34398 Montpellier 5 France INRA MIA ENGREF UMR INA PG F-75231 Paris France INRA Unite EPHYSE Ecol Fonct & Phys Environm F-33612 Cestas France

Observed growth, as given, for instance, by the length of successive annual shoots along the main axis of a plant, is mainly the result of two components: an ontogenetic component and an environmental component. An open question is whether the ontogenetic component along an axis at the growth unit or annual shoot scale takes the form of a trend or of a succession of phases. Various methods of analysis ranging from exploratory analysis (symmetric smoothing filters, sample autocorrelation functions) to statistical modeling (multiple change-point models, hidden semi-Markov chains and hidden hybrid model combining Markovian and semi-Markovian states) are applied to extract and characterize both the ontogenetic and environmental components using contrasted examples. This led us in particular to favor the hypothesis of an ontogenetic component structured as a succession of stationary phases and to highlight phase changes of high magnitude in unexpected situations (for instance, when growth globally decreases). These results shed light in a new way on botanical concepts such as "phase change" and "morphogenetic gradient". (C) 2007 Elsevier Ltd. All rights reserved.

关键词： autocorrelation function hidden semi-Markov chain linear filtering morphogenetic gradient multiple change-point detection ontogeny phase change plant architecture

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