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Method for selecting representative videos for change detection datasets

MULTIMEDIA TOOLS AND APPLICATIONS 2022年第3期81卷 3773-3791页

作者： Silva, Claudinei M. Rosa, Katharina A., I Bugatti, Pedro H. Saito, Priscila T. M. Correa, Cleber G. Yokoyama, Roberto S. Sanches, Silvio R. R. Univ Tecnol Fed Parana Cornelio Procopio Brazil Univ Fed ABC Santo Andre SP Brazil

In evaluating the change detection algorithms, the algorithm evaluated must show a superior performance than the state-of-the-art algorithms. The evaluation process steps comprise executing a new algorithm to segment a set of videos from a dataset and compare the results regarding a ground truth. In this paper, we propose using additional information in evaluating change detection algorithms: the level of difficulty in classifying a pixel. First, for each video frame used in the evaluation, we created a difficulty map structure, which stores values representing the level of difficulty required by an algorithm to classify each pixel of that frame. Second, we developed a metric to estimate each dataset video's difficulty based on our difficulty maps. Third, we applied the metric to selecting the more representative videos from the dataset based on their difficulty level. Finally, to demonstrate the method's contribution, we evaluated it using all videos from the CDNet 2014 dataset. The results showed that a subset of videos selected by our method has the same potential as the original CDNet 2014 dataset. Hence, a new change detection algorithm can be evaluated more quickly using our subset of videos selected.

关键词： Change detection Dataset algorithm evaluation

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An efficient simplex type algorithm for sparse and dense linear programs

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2003年第2期148卷 323-334页

作者： Paparrizos, K Samaras, N Stephanides, G Univ Macedonia Dept Appl Informat Thessaloniki 54006 Greece

We present a big-M method for solving general linear programming problems with a recently developed exterior point simplex algorithm (EPSA). We also provide intuitive motivations to EPSA. We describe major steps in implementing EPSA for large-scale linear programming. Our implementation was carried out under the MATLAB environment. This algorithm seems to be more efficient than the classical primal simplex algorithm (PSA), employing Dantzig's rule. Preliminary computational studies on randomly generated sparse and dense small and medium linear programs support this belief. In particular, EPSA is up to 10 times faster than PSA on medium size linear programs. This translates into corresponding savings in CPU time. Although the computational effort required in each step of EPSA requires more time compared to an iteration step of PSA, the improvement of EPSA comes from the fact that it requires adequately less iterations than PSA. Moreover, as the problem size increases and the problem density decreases, EPSA gets relatively faster. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： linear programming simplex type algorithms big-M method algorithm evaluation numerical experiments

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A quantitative study of experimental evaluations of neural network learning algorithms: Current research practice

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NEURAL NETWORKS 1996年第3期9卷 457-462页

作者： Prechelt, L Universit?t Karlsruhe Germany

In all, 190 articles about neural network learning algorithms published in 1993 and 1994 are examined for the amount of experimental evaluation they contain. Some 29% of them employ not even a single realistic or real learning problem. Only 8% of the articles present results for more than one problem using real world data. Furthermore, one third of all articles do not present any quantitative comparison with a previously known algorithm. These results suggest that we should strive for better assessment practices in neural network learning algorithm research. For the long-term benefit of the field, the publication standards should be raised in this respect and easily accessible collections of benchmark problems should be built. Copyright (C) 1996 Elsevier Science Ltd

关键词： algorithm evaluation science experiment

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Scalable process discovery and conformance checking

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SOFTWARE AND SYSTEMS MODELING 2018年第2期17卷 599-631页

作者： Leemans, Sander J. J. Fahland, Dirk Van der Aalst, Wil M. P. Eindhoven Univ Technol Eindhoven Netherlands

Considerable amounts of data, including process events, are collected and stored by organisations nowadays. Discovering a process model from such event data and verification of the quality of discovered models are important steps in process mining. Many discovery techniques have been proposed, but none of them combines scalability with strong quality guarantees. We would like such techniques to handle billions of events or thousands of activities, to produce sound models (without deadlocks and other anomalies), and to guarantee that the underlying process can be rediscovered when sufficient information is available. In this paper, we introduce a framework for process discovery that ensures these properties while passing over the log only once and introduce three algorithms using the framework. To measure the quality of discovered models for such large logs, we introduce a model-model and model-log comparison framework that applies a divide-and-conquer strategy to measure recall, fitness, and precision. We experimentally show that these discovery and measuring techniques sacrifice little compared to other algorithms, while gaining the ability to cope with event logs of 100,000,000 traces and processes of 10,000 activities on a standard computer.

关键词： Big data Scalable process mining Block-structured process discovery Directly follows graphs algorithm evaluation Rediscoverability Conformance checking

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A MCDM-Based evaluation Approach for Imbalanced Classification Methods in Financial Risk Prediction

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IEEE ACCESS 2019年 7卷 84897-84906页

作者： Song, Yongming Peng, Yi Shandong Technol & Business Univ Sch Business Adm Yantai 264005 Peoples R China Univ Elect Sci & Technol China Sch Management & Econ Chengdu 611731 Sichuan Peoples R China

Various classifiers have been proposed for financial risk prediction. The traditional practice of using a singular performance metric for classifier evaluation is not sufficient for imbalanced classification. This paper proposes a multi-criteria decision making (MCDM)-based approach to evaluate imbalanced classifiers in credit and bankruptcy risk prediction by considering multiple performance metrics simultaneously. An experimental study is designed to provide a comprehensive evaluation of imbalanced classifiers using the proposed evaluation approach over seven financial imbalanced data sets from the UCI Machine Learning Repository. The TOPSIS, a well-known MCDM method, was applied to rank three categories of imbalanced classifiers using six popular evaluation criteria. The rankings results indicate that: 1) the rankings generated by the TOPSIS, which combine the results of six evaluation criteria, provide a more reasonable evaluation of imbalanced classifiers over any single performance criterion;and 2) Synthetic Minority Oversampling Technique (SMOTE)-based ensemble techniques outperform other groups of imbalanced learning approaches. Specifically, SMOTEBoost-C4.5, SMOTE-C4.5, and SMOTE-MLP were ranked as the top three classifiers based on their performances on the six criteria.

关键词： Financial risk prediction imbalanced classification multiple criteria decision making (MCDM) algorithm evaluation

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evaluation of Change Detection algorithms using Difficulty Maps

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IEEE LATIN AMERICA TRANSACTIONS 2023年第6期21卷 700-706页

作者： Sanches, Silvio Ricardo Rodrigues Correa, Cleber Gimenez Brum, Beatriz Regina Bugatti, Pedro Henrique Saito, Priscila Tiemi Maeda da Silva, Claudinei Moreira Custodio Junior, Elton Univ Tecnol Fed Parana Cornelio Procopio Brazil Univ Fed Sao Carlos Sao Carlos Brazil

The evaluation of a change detection algorithm should show its superiority over state-of-the-art algorithms' performances. Evaluating an algorithm involves executing it to segment a set of videos and comparing the results with the ground truth. Here, we used the difficulty level to classify each pixel of each frame of the videos of a dataset as an algorithm performance measure. A structure called "difficulty map" stores information about the difficulty of classifying each pixel in a frame. Based on these maps, we developed a metric that aims to evaluate the performance of algorithms on the difficulty map. The results showed that there are algorithms with the characteristic of classifying pixels that most state-of-the-art algorithms cannot classify (promising algorithms). Identifying such algorithms is essential since improving their performance means facing challenges already overcome by existing approaches.

关键词： Classification algorithms Videos Detection algorithms Change detection algorithms IEEE transactions Monitoring Meteorology algorithm evaluation change detection difficulty map video segmentation

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Data envelopment analysis for evaluating the efficiency of genetic algorithms on solving the vehicle routing problem with soft time windows

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COMPUTERS & INDUSTRIAL ENGINEERING 2012年第2期63卷 520-529页

作者： Lu, Chung-Cheng Yu, Vincent F. Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei 106 Taiwan Natl Taipei Univ Technol Grad Inst Informat & Logist Management Taipei 106 Taiwan

This study proposes an alternative to the conventional empirical analysis approach for evaluating the relative efficiency of distinct combinations of algorithmic operators and/or parameter values of genetic algorithms (GAS) on solving the pickup and delivery vehicle routing problem with soft time windows (PDVRPSTW). Our approach considers each combination as a decision-making unit (DMU) and adopts data envelopment analysis (DEA) to determine the relative and cross efficiencies of each combination of GA operators and parameter values on solving the PDVRPSTW. To demonstrate the applicability and advantage of this approach, we implemented a number of combinations of GA's three main algorithmic operators, namely selection, crossover and mutation, and employed DEA to evaluate and rank the relative efficiencies of these combinations. The numerical results show that DEA is well suited for determining the efficient combinations of GA operators. Among the combinations under consideration, the combinations using tournament selection and simple crossover are generally more efficient. The proposed approach can be adopted to evaluate the relative efficiency of other meta-heuristics, so it also contributes to the algorithm development and evaluation for solving combinatorial optimization problems from the operational research perspective. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： algorithm evaluation Vehicle routing problem Genetic algorithm Data envelopment analysis Pickup and delivery Soft time windows

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EnsembleLens: Ensemble-based Visual Exploration of Anomaly Detection algorithms with Multidimensional Data

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2019年第1期25卷 109-119页

作者： Xu, Ke Xia, Meng Mu, Xing Wang, Yun Cao, Nan Hong Kong Univ Sci & Technol Hong Kong Peoples R China Tongji Univ IDVx Lab Shanghai Peoples R China

The results of anomaly detection are sensitive to the choice of detection algorithms as they are specialized for different properties of data, especially for multidimensional data. Thus, it is vital to select the algorithm appropriately. To systematically select the algorithms, ensemble analysis techniques have been developed to support the assembly and comparison of heterogeneous algorithms. However, challenges remain due to the absence of the ground truth, interpretation, or evaluation of these anomaly detectors. In this paper, we present a visual analytics system named EnsembleLens that evaluates anomaly detection algorithms based on the ensemble analysis process. The system visualizes the ensemble processes and results by a set of novel visual designs and multiple coordinated contextual views to meet the requirements of correlation analysis, assessment and reasoning of anomaly detection algorithms. We also introduce an interactive analysis workflow that dynamically produces contextualized and interpretable data summaries that allow further refinements of exploration results based on user feedback. We demonstrate the effectiveness of EnsembleLens through a quantitative evaluation, three case studies with real-world data and interviews with two domain experts.

关键词： algorithm evaluation Ensemble Analysis Anomaly Detection Visual Analysis Multidimensional Data

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A Comprehensive Analysis Method for Reversible Data Hiding in Stream-Cipher-Encrypted Images

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2022年第10期32卷 7241-7254页

作者： Yu, Mingji Yao, Heng Qin, Chuan Zhang, Xinpeng Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai 200093 Peoples R China Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China

Reversible data hiding in encrypted images (RDHEI), an essential branch of reversible data hiding (RDH), has been in development for more than a decade. For most existing stream-cipher-based RDHEI algorithms, encryption schemes are often not the same;thus, these schemes have different effects on the encrypted images. As a result, it is not reasonable to compare the embedding rates (ERs) of RDHEI algorithms directly as we do for plaintext RDH algorithms. However, to our knowledge, many studies focus on the performance of embedding but neglect the influence of the encryption. To compare the performance of stream-cipher-based RDHEI algorithms more reasonably, this paper proposes a novel comprehensive measure to evaluate state-of-the-art RDHEI algorithms. First, the characteristics of the stream-cipher-based RDHEI algorithms are divided into two categories according to the encryption and embedding processes. Next, we use correlation and redundancy to evaluate the influence of encryption schemes and also use ER, computation complexity, visual quality, and algorithm stability to evaluate the embedding schemes. In the end, in order to combine all six indexes into the final evaluation measure, we standardize each index before applying the radar chart. The experimental results show the evaluation results of the recent RDHEI algorithms via a comprehensive analysis and demonstrate the guiding significance of the proposed method for the current RDHEI algorithm selection.

关键词： Correlation Indexes Streaming media Prediction algorithms Homomorphic encryption Histograms Ciphers Reversible data hiding encryption images stream cipher encryption algorithm evaluation

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Using Landsat to extend the historical record of lacustrine phytoplankton blooms: A Lake Erie case study

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REMOTE SENSING OF ENVIRONMENT 2017年 191卷 273-285页

作者： Ho, Jeff C. Stumpf, Richard P. Bridgeman, Thomas B. Michalak, Anna M. Stanford Univ Dept Civil & Environm Engn 473 Via Ortega Stanford CA 94305 USA Carnegie Inst Sci Dept Global Ecol 260 Panama St Stanford CA 94305 USA NOAA Natl Centers Coastal Ocean Sci 1305 East West Highway code N SCI1 Silver Spring MD 20910 USA Univ Toledo Dept Environm Sci & Lake Erie Ctr 6200 Bayshore Rd Bayshore Rd OR 43616 USA

Long-term records of phytoplankton blooms in freshwater lakes are necessary both for understanding basin scale changes to watersheds and for providing a key constraint for assessing processes driving blooms. However, due to the inherent constraints of in situ sampling and the short time period covered by modem space borne sensors, few long-term records exist. Historical data from sensors such as Landsat offer strong potential for creating new records of past blooms. Here, we use a novel evaluation procedure based on multiple metrics to assess algorithm suitability and robustness for generating long-term bloom records using Landsat 5 imagery. evaluation metrics are based on bloom presence, spatial distribution, magnitude and timing, using both in situ Microcystis biovolume and remotely-sensed Cyanobacterial Index (CI) data from MERIS for 2002-2011. Applying this procedure for a test case focusing on Lake Erie's western basin, an algorithm based on a near infrared threshold with simple atmospheric correction through subtraction of the shortwave infrared band, combined with an additional "greenness" filter based on a hue threshold, performs best. Implementing this algorithm for 1984-2001 reveals the long-term trends in peak bloom magnitude prior to the start of the MERIS and MODIS record (2002-2015), and more than doubles the period of record that can be used to understand bloom occurrence and growth for this system. More broadly, we demonstrate that Landsat observations can be used to identify macro-scale features of blooms. For Lake Erie, the performance of the final Landsat algorithm is comparable to that of the MERIS CI algorithm, despite Landsat's broad spectral bands and long revisit time. (C) 2017 The Authors. Published by Elsevier Inc.

关键词： Landsat Lake Erie Harmful algal bloom Phytoplanlcton Cyanobacteria Freshwater Long-term data algorithm evaluation Hindcasting

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