首页 > 最新文献

2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)最新文献

英文 中文
Selecting Feature-Words in Tag Sense Disambiguation Based on Their Shapley Value 基于Shapley值的标记义消歧特征词选择
Meshesha Legesse, G. Gianini, Dereje Teferi
In tag-word disambiguation, a word is assigned to a specific context chosen among the different ones to which it is related. Relatedness to a context is often defined based on the co-occurrence of the target word with other words (context words) in sentences of a specific corpus. The overall disambiguation process can be thought as a classification process, where the context words play the role of features for the target. A problem with this approach is that the large number of possible context words can reduce the classification performance, both in terms of computational effort and in terms of quality of the outcome. Feature selection can improve the process in both regards, by reducing the overall feature space to a manageable size with high information content. In this work we propose to use, in disambiguation, a feature selection approach based on the Shapley Value (SV) - a Coalitional Game Theory related metrics, measuring the importance of a component within a coalition. By including in the feature set only the words with the highest Shapley Value, we obtain remarkable quality and performance improvements. The problem of the exponential complexity in the exact SV computation is avoided by an approximate computation based on sampling. We demonstrate the effectiveness of this method and of the sampling approach results, by using both a synthetic language corpus and a real world linguistic corpus.
在标签词消歧中,一个词被分配到从与之相关的不同上下文中选择的特定上下文中。与上下文的相关性通常是根据目标词与特定语料库句子中的其他词(上下文词)的共现来定义的。整个消歧过程可以看作是一个分类过程,其中语境词对目标词起着特征作用。这种方法的一个问题是,大量可能的上下文词会降低分类性能,无论是在计算工作量方面还是在结果质量方面。特征选择可以通过将整体特征空间减小到具有高信息量的可管理大小来改善这两个方面的过程。在这项工作中,我们建议在消除歧义时使用基于Shapley值(SV)的特征选择方法-一种与联盟博弈论相关的度量,测量联盟中组件的重要性。通过在特征集中只包含Shapley值最高的单词,我们获得了显著的质量和性能改进。通过基于采样的近似计算,避免了精确SV计算中的指数复杂度问题。我们通过使用一个合成语料库和一个真实世界的语料库来证明这种方法和抽样方法结果的有效性。
{"title":"Selecting Feature-Words in Tag Sense Disambiguation Based on Their Shapley Value","authors":"Meshesha Legesse, G. Gianini, Dereje Teferi","doi":"10.1109/SITIS.2016.45","DOIUrl":"https://doi.org/10.1109/SITIS.2016.45","url":null,"abstract":"In tag-word disambiguation, a word is assigned to a specific context chosen among the different ones to which it is related. Relatedness to a context is often defined based on the co-occurrence of the target word with other words (context words) in sentences of a specific corpus. The overall disambiguation process can be thought as a classification process, where the context words play the role of features for the target. A problem with this approach is that the large number of possible context words can reduce the classification performance, both in terms of computational effort and in terms of quality of the outcome. Feature selection can improve the process in both regards, by reducing the overall feature space to a manageable size with high information content. In this work we propose to use, in disambiguation, a feature selection approach based on the Shapley Value (SV) - a Coalitional Game Theory related metrics, measuring the importance of a component within a coalition. By including in the feature set only the words with the highest Shapley Value, we obtain remarkable quality and performance improvements. The problem of the exponential complexity in the exact SV computation is avoided by an approximate computation based on sampling. We demonstrate the effectiveness of this method and of the sampling approach results, by using both a synthetic language corpus and a real world linguistic corpus.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134136904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
Random Forest for Salary Prediction System to Improve Students' Motivation 随机森林薪酬预测系统提高学生学习动机
Pornthep Khongchai, Pokpong Songmuang
A salary prediction model was generated for graduate students using a data mining technique to generate for individuals with similar training attributes. An experiment was also conducted to compare the two data mining techniques Decision Trees ID3, C4.5 and Random Forest to determine the most suitable technique for salary prediction, tuned with key important parameters to improve the accuracy of the results. Random Forest gave the best accuracy at 90.50%, while Decision Trees ID3 and C4.5 returned lower accuracies at 61.37% and 73.96%, respectively for 13,541 records of graduate students using a 10-fold cross-validation method. Random Forest generated the best efficiency model for salary prediction. A questionnaire survey was conducted to determine usage evaluation with 50 samples. Results indicated that the system was effective in boosting students' motivation for studying, and also gave them a positive future viewpoint. The results also suggested that the students were satisfied with the implemented system since it was easy to use, and the prediction results were simple to understand without any previous background statistical knowledge.
使用数据挖掘技术生成具有相似训练属性的个人的研究生工资预测模型。实验还比较了两种数据挖掘技术决策树ID3、C4.5和随机森林,以确定最适合的工资预测技术,并对关键重要参数进行了调整,以提高结果的准确性。使用10倍交叉验证方法,随机森林的准确率最高,为90.50%,而决策树ID3和C4.5的准确率较低,分别为61.37%和73.96%,用于13,541条研究生记录。随机森林生成了工资预测的最佳效率模型。采用问卷调查法对50个样本进行使用评价。结果表明,该系统有效地提高了学生的学习动机,并使他们对未来有了积极的看法。结果还表明,学生对实施的系统感到满意,因为它易于使用,预测结果简单易懂,无需任何背景统计知识。
{"title":"Random Forest for Salary Prediction System to Improve Students' Motivation","authors":"Pornthep Khongchai, Pokpong Songmuang","doi":"10.1109/SITIS.2016.106","DOIUrl":"https://doi.org/10.1109/SITIS.2016.106","url":null,"abstract":"A salary prediction model was generated for graduate students using a data mining technique to generate for individuals with similar training attributes. An experiment was also conducted to compare the two data mining techniques Decision Trees ID3, C4.5 and Random Forest to determine the most suitable technique for salary prediction, tuned with key important parameters to improve the accuracy of the results. Random Forest gave the best accuracy at 90.50%, while Decision Trees ID3 and C4.5 returned lower accuracies at 61.37% and 73.96%, respectively for 13,541 records of graduate students using a 10-fold cross-validation method. Random Forest generated the best efficiency model for salary prediction. A questionnaire survey was conducted to determine usage evaluation with 50 samples. Results indicated that the system was effective in boosting students' motivation for studying, and also gave them a positive future viewpoint. The results also suggested that the students were satisfied with the implemented system since it was easy to use, and the prediction results were simple to understand without any previous background statistical knowledge.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134429629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 12
Fast Range Image Registration by an Asynchronous Adaptive Distributed Differential Evolution 基于异步自适应分布式差分进化的快速距离图像配准
I. D. Falco, A. D. Cioppa, U. Scafuri, E. Tarantino
In this paper the application of a general-purpose distributed Differential Evolution algorithm to range image registration is presented. The algorithm is characterized by an asynchronous migration mechanism and by a multi-population recombination information exchange, and is also supplied with adaptive updating schemes for automatically setting the Differential Evolution control parameters. In particular, this algorithm has been employed to tackle the problem of the pair-wise range image registration. Given two images with the first set as the model, the scope is to find the best possible spatial transformation of the second image allowing for 3D reconstruction of the original model. Experimental findings demonstrate the ability of such an adaptive algorithm in finding out efficient image transformations. A comparison of the results with those attained by recently presented evolutionary algorithms show the effectiveness of the proposed approach in terms of both quality and robustness of the reconstructed 3D image, and of computational cost.
本文提出了一种通用的分布式差分进化算法在距离图像配准中的应用。该算法具有异步迁移机制和多种群重组信息交换的特点,并提供了自动设置差分进化控制参数的自适应更新方案。特别地,该算法被用于解决图像的成对范围配准问题。给定以第一组为模型的两幅图像,其范围是找到第二幅图像的最佳空间变换,以便对原始模型进行3D重建。实验结果证明了该自适应算法在寻找有效的图像变换方面的能力。结果表明,该方法在重建三维图像的质量和鲁棒性以及计算成本方面都是有效的。
{"title":"Fast Range Image Registration by an Asynchronous Adaptive Distributed Differential Evolution","authors":"I. D. Falco, A. D. Cioppa, U. Scafuri, E. Tarantino","doi":"10.1109/SITIS.2016.107","DOIUrl":"https://doi.org/10.1109/SITIS.2016.107","url":null,"abstract":"In this paper the application of a general-purpose distributed Differential Evolution algorithm to range image registration is presented. The algorithm is characterized by an asynchronous migration mechanism and by a multi-population recombination information exchange, and is also supplied with adaptive updating schemes for automatically setting the Differential Evolution control parameters. In particular, this algorithm has been employed to tackle the problem of the pair-wise range image registration. Given two images with the first set as the model, the scope is to find the best possible spatial transformation of the second image allowing for 3D reconstruction of the original model. Experimental findings demonstrate the ability of such an adaptive algorithm in finding out efficient image transformations. A comparison of the results with those attained by recently presented evolutionary algorithms show the effectiveness of the proposed approach in terms of both quality and robustness of the reconstructed 3D image, and of computational cost.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133927979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
Tracking People in Dense Crowds Using Supervoxels 使用超体素跟踪密集人群中的人
Shota Takayama, Teppei Suzuki, Y. Aoki, S. Isobe, Makoto Masuda
The demand for people tracking in dense crowds is increasing, but it is a challenging problem in the computer vision field. "Crowd tracking" is extremely difficult because of hard occlusions, various motions and posture changes. In particular, we need to handle occlusions for more robust tracking. This paper discusses robust crowd tracking based on a combination of supervoxels and optical flow tracking. The SLIC based supervoxel algorithm adaptively estimates the boundary between a person and a background. Therefore, the combination of supervoxels and optical flow tracking becomes a highly reliable approach for crowd tracking. In tracking experiments, high performance is achieved for the UCF crowd dataset.
在密集人群中跟踪人的需求日益增加,但这是计算机视觉领域的一个具有挑战性的问题。“人群跟踪”是非常困难的,因为硬闭塞,各种动作和姿势的变化。特别是,我们需要处理遮挡以实现更稳健的跟踪。本文讨论了基于超体素和光流跟踪相结合的鲁棒人群跟踪方法。基于SLIC的超体素算法自适应估计人与背景之间的边界。因此,超体素和光流跟踪相结合成为一种高度可靠的人群跟踪方法。在跟踪实验中,UCF人群数据集取得了较高的性能。
{"title":"Tracking People in Dense Crowds Using Supervoxels","authors":"Shota Takayama, Teppei Suzuki, Y. Aoki, S. Isobe, Makoto Masuda","doi":"10.1109/SITIS.2016.90","DOIUrl":"https://doi.org/10.1109/SITIS.2016.90","url":null,"abstract":"The demand for people tracking in dense crowds is increasing, but it is a challenging problem in the computer vision field. \"Crowd tracking\" is extremely difficult because of hard occlusions, various motions and posture changes. In particular, we need to handle occlusions for more robust tracking. This paper discusses robust crowd tracking based on a combination of supervoxels and optical flow tracking. The SLIC based supervoxel algorithm adaptively estimates the boundary between a person and a background. Therefore, the combination of supervoxels and optical flow tracking becomes a highly reliable approach for crowd tracking. In tracking experiments, high performance is achieved for the UCF crowd dataset.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133992132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Longitudinal Neuroimaging Analysis Using Non-Negative Matrix Factorization 非负矩阵分解纵向神经成像分析
C. Stamile, F. Cotton, D. Sappey-Marinier, S. Huffel
Longitudinal analysis of neuroimaging data is becoming an important research area. In the last few years analysis of longitudinal data become a crucial point to better understand pathological mechanisms of complex brain diseases such as multiple sclerosis (MS) where white matter (WM) fiber bundles are variably altered by inflammatory events. In this work, we propose a new fully automated method to detect significant longitudinal changes in diffusivity metrics along WM fiber-bundles. This method consists of two steps: i) preprocessing of longitudinal diffusion acquisitions and WM fiber-bundles extraction, ii) application of a new hierarchical non negative matrix factorization (hNMF) algorithm to detect "pathological" changes. This method was applied first, on simulated longitudinal variations, and second, on MS patients longitudinal data. High level of precision, recall and F-Measure were obtained for the detection of small longitudinal changes along the WM fiber-bundles.
神经影像学数据的纵向分析正成为一个重要的研究领域。在过去的几年里,纵向数据的分析成为更好地理解复杂脑部疾病的病理机制的关键点,如多发性硬化症(MS),其中白质(WM)纤维束因炎症事件而发生变化。在这项工作中,我们提出了一种新的全自动方法来检测沿WM纤维束扩散系数指标的显著纵向变化。该方法包括两个步骤:i)纵向扩散采集和WM纤维束提取的预处理,ii)应用新的分层非负矩阵分解(hNMF)算法来检测“病理”变化。该方法首先应用于模拟的纵向变化,其次应用于MS患者的纵向数据。对于沿WM纤维束的微小纵向变化的检测,获得了高水平的精度,召回率和F-Measure。
{"title":"Longitudinal Neuroimaging Analysis Using Non-Negative Matrix Factorization","authors":"C. Stamile, F. Cotton, D. Sappey-Marinier, S. Huffel","doi":"10.1109/SITIS.2016.18","DOIUrl":"https://doi.org/10.1109/SITIS.2016.18","url":null,"abstract":"Longitudinal analysis of neuroimaging data is becoming an important research area. In the last few years analysis of longitudinal data become a crucial point to better understand pathological mechanisms of complex brain diseases such as multiple sclerosis (MS) where white matter (WM) fiber bundles are variably altered by inflammatory events. In this work, we propose a new fully automated method to detect significant longitudinal changes in diffusivity metrics along WM fiber-bundles. This method consists of two steps: i) preprocessing of longitudinal diffusion acquisitions and WM fiber-bundles extraction, ii) application of a new hierarchical non negative matrix factorization (hNMF) algorithm to detect \"pathological\" changes. This method was applied first, on simulated longitudinal variations, and second, on MS patients longitudinal data. High level of precision, recall and F-Measure were obtained for the detection of small longitudinal changes along the WM fiber-bundles.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125204201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
On the Use of Belief Functions to Improve High Performance Intrusion Detection System 利用信念函数改进高性能入侵检测系统
Alem Abdelkader, Y. Dahmani, A. Hadjali
Dempster-Shafer theory is a very powerful tool for data fusion, which provides a good estimation of imprecision, conflict from different sources and deal with any unions of hypotheses. In this paper, we propose to develop a high-performance hybrid Network Intrusion Detection System, based on belief functions. This system contains three levels, the first one includes two fast classifiers: Naïve Bayes and Support Vector Machine (SVM) Bused for their performance on classification. In the second level outputs of both SVM and Naïve Bayes are fuzzified using fuzzy logic. Third, the overall decision of the system is performed using Dempster's rule of combination. The experimentation on a recent benchmark dataset shows that our approach achieves a higher detection rate with low false alarm rates compared to some existing classifiers.
Dempster-Shafer理论是一个非常强大的数据融合工具,它提供了一个很好的估计不精确,来自不同来源的冲突和处理任何合并的假设。本文提出了一种基于信念函数的高性能混合网络入侵检测系统。该系统包含三个层次,第一个层次包括两个快速分类器:Naïve基于贝叶斯和支持向量机(SVM)的分类性能。在第二层,使用模糊逻辑对SVM和Naïve贝叶斯的输出进行模糊化。第三,采用Dempster组合规则对系统进行总体决策。在最近的一个基准数据集上的实验表明,与现有的一些分类器相比,我们的方法在低误报率的情况下实现了更高的检测率。
{"title":"On the Use of Belief Functions to Improve High Performance Intrusion Detection System","authors":"Alem Abdelkader, Y. Dahmani, A. Hadjali","doi":"10.1109/SITIS.2016.50","DOIUrl":"https://doi.org/10.1109/SITIS.2016.50","url":null,"abstract":"Dempster-Shafer theory is a very powerful tool for data fusion, which provides a good estimation of imprecision, conflict from different sources and deal with any unions of hypotheses. In this paper, we propose to develop a high-performance hybrid Network Intrusion Detection System, based on belief functions. This system contains three levels, the first one includes two fast classifiers: Naïve Bayes and Support Vector Machine (SVM) Bused for their performance on classification. In the second level outputs of both SVM and Naïve Bayes are fuzzified using fuzzy logic. Third, the overall decision of the system is performed using Dempster's rule of combination. The experimentation on a recent benchmark dataset shows that our approach achieves a higher detection rate with low false alarm rates compared to some existing classifiers.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125053812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
Image Problem Classification for Dashboard Cameras 仪表板摄像头图像问题分类
Narit Hnoohom, Thanchanok Thanapattherakul
This paper aimed to develop a prediction model to classify problems arising in images obtained from dashboard camera video by using machine-learning algorithms. The authors generated a dataset, called the DS dataset, which contained 900 images. The dataset was divided into three groups of problems comprised of lightness problems, a combination of lightness and blur problems, and a combination of lightness and noise problems. In this study, five features on the dataset were utilised, including mean, standard deviation, entropy, histogram, and variance of the images. Classification was performed on 3 machine-learning algorithms, which were Decision Tree, Naïve Bayes and Support Vector Machines on images and partitions of the images. The experimental results showed that decision tree algorithm yielded the best performance in comparison with the two other algorithms, with the optimal prediction model obtaining an accuracy rate of up to 97.88 percent.
本文旨在开发一种预测模型,利用机器学习算法对仪表盘摄像头视频图像中出现的问题进行分类。作者生成了一个名为DS数据集的数据集,其中包含900张图像。数据集被分为三组问题,包括亮度问题,亮度和模糊问题的组合,以及亮度和噪声问题的组合。在本研究中,利用了数据集的五个特征,包括图像的均值、标准差、熵、直方图和方差。使用决策树、Naïve贝叶斯和支持向量机3种机器学习算法对图像进行分类,并对图像进行分区。实验结果表明,与其他两种算法相比,决策树算法的性能最好,最优预测模型的准确率高达97.88%。
{"title":"Image Problem Classification for Dashboard Cameras","authors":"Narit Hnoohom, Thanchanok Thanapattherakul","doi":"10.1109/SITIS.2016.112","DOIUrl":"https://doi.org/10.1109/SITIS.2016.112","url":null,"abstract":"This paper aimed to develop a prediction model to classify problems arising in images obtained from dashboard camera video by using machine-learning algorithms. The authors generated a dataset, called the DS dataset, which contained 900 images. The dataset was divided into three groups of problems comprised of lightness problems, a combination of lightness and blur problems, and a combination of lightness and noise problems. In this study, five features on the dataset were utilised, including mean, standard deviation, entropy, histogram, and variance of the images. Classification was performed on 3 machine-learning algorithms, which were Decision Tree, Naïve Bayes and Support Vector Machines on images and partitions of the images. The experimental results showed that decision tree algorithm yielded the best performance in comparison with the two other algorithms, with the optimal prediction model obtaining an accuracy rate of up to 97.88 percent.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129250782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Conversational Group Detection Based on Social Context Using Graph Clustering Algorithm 基于社会语境的会话组检测——基于图聚类算法
Shoichi Inaba, Y. Aoki
With the development of single-person analysis in computer vision, social group analysis has received growing attention as the next area of research. In particular, group detection has been actively studied as the first step of social analysis. Here, group means an F-formation, that is, a spatial organization of people gathered for conversation. Popular group detection methods are based on coincidences in the visual attention field that are calculated from the position and body orientation of the individuals in the group. However, most previous studies have assumed that each member has the same visual attention field, and they do not consider changes in the scene over time. In this paper, we present a robust method for detection of time-varying F-formations in social space, its visual attention field model is based on the local environment. We present the results of an experiment that uses a dataset of multiple scenes, an analysis of these results validates the advantages of our method.
随着计算机视觉中单人分析的发展,社会群体分析作为下一个研究领域受到越来越多的关注。特别是,群体检测作为社会分析的第一步被积极研究。在这里,group指的是f字形,即人们聚集在一起进行交谈的空间组织。流行的群体检测方法是基于从群体中个体的位置和身体方向计算的视觉注意领域的巧合。然而,大多数先前的研究都假设每个成员都有相同的视觉注意领域,并且他们没有考虑场景随时间的变化。本文提出了一种基于局部环境的视觉注意场模型,用于社会空间中时变f形的鲁棒检测。我们给出了一个使用多个场景数据集的实验结果,对这些结果的分析验证了我们的方法的优势。
{"title":"Conversational Group Detection Based on Social Context Using Graph Clustering Algorithm","authors":"Shoichi Inaba, Y. Aoki","doi":"10.1109/SITIS.2016.89","DOIUrl":"https://doi.org/10.1109/SITIS.2016.89","url":null,"abstract":"With the development of single-person analysis in computer vision, social group analysis has received growing attention as the next area of research. In particular, group detection has been actively studied as the first step of social analysis. Here, group means an F-formation, that is, a spatial organization of people gathered for conversation. Popular group detection methods are based on coincidences in the visual attention field that are calculated from the position and body orientation of the individuals in the group. However, most previous studies have assumed that each member has the same visual attention field, and they do not consider changes in the scene over time. In this paper, we present a robust method for detection of time-varying F-formations in social space, its visual attention field model is based on the local environment. We present the results of an experiment that uses a dataset of multiple scenes, an analysis of these results validates the advantages of our method.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129476144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 14
Semantic and Visual Cues for Humanitarian Computing of Natural Disaster Damage Images 自然灾害损害图像人道主义计算的语义和视觉线索
H. Jomaa, Yara Rizk, M. Awad
Identifying different types of damage is very essential in times of natural disasters, where first responders are flooding the internet with often annotated images and texts, and rescue teams are overwhelmed to prioritize often scarce resources. While most of the efforts in such humanitarian situations rely heavily on human labor and input, we propose in this paper a novel hybrid approach to help automate more humanitarian computing. Our framework merges low-level visual features that extract color, shape and texture along with a semantic attribute that is obtained after comparing the picture annotation to some bag of words. These visual and textual features were trained and tested on a dataset gathered from the SUN database and some Google Images. The best accuracy obtained using low-level features alone is 91.3 %, while appending the semantic attributes to it raised the accuracy to 95.5% using linear SVM and 5-Fold cross-validation which motivates an updated folk statement "an ANNOTATED image is worth a thousand word ".
在自然灾害发生时,识别不同类型的损害是非常重要的,在这种情况下,第一响应者在互联网上充斥着通常带有注释的图像和文本,而救援队则不堪重负,无法优先考虑往往稀缺的资源。虽然在这种人道主义情况下的大多数努力严重依赖于人类劳动和投入,但我们在本文中提出了一种新的混合方法来帮助自动化更多的人道主义计算。我们的框架将提取颜色、形状和纹理的低级视觉特征与将图片注释与一些单词进行比较后获得的语义属性合并在一起。这些视觉和文本特征在从SUN数据库和一些Google Images收集的数据集上进行了训练和测试。仅使用低级特征获得的最佳准确率为91.3%,而使用线性支持向量机和5-Fold交叉验证将其添加到语义属性将准确率提高到95.5%,从而激发了更新的民间语句“注释图像胜过千言万语”。
{"title":"Semantic and Visual Cues for Humanitarian Computing of Natural Disaster Damage Images","authors":"H. Jomaa, Yara Rizk, M. Awad","doi":"10.1109/SITIS.2016.70","DOIUrl":"https://doi.org/10.1109/SITIS.2016.70","url":null,"abstract":"Identifying different types of damage is very essential in times of natural disasters, where first responders are flooding the internet with often annotated images and texts, and rescue teams are overwhelmed to prioritize often scarce resources. While most of the efforts in such humanitarian situations rely heavily on human labor and input, we propose in this paper a novel hybrid approach to help automate more humanitarian computing. Our framework merges low-level visual features that extract color, shape and texture along with a semantic attribute that is obtained after comparing the picture annotation to some bag of words. These visual and textual features were trained and tested on a dataset gathered from the SUN database and some Google Images. The best accuracy obtained using low-level features alone is 91.3 %, while appending the semantic attributes to it raised the accuracy to 95.5% using linear SVM and 5-Fold cross-validation which motivates an updated folk statement \"an ANNOTATED image is worth a thousand word \".","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"106 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120994865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Action Recognition Online with Hierarchical Self-Organizing Maps 基于层次自组织地图的在线动作识别
Zahra Gharaee, P. Gärdenfors, Magnus Johnsson
We present a hierarchical self-organizing map based system for online recognition of human actions. We have made a first evaluation of our system by training it on two different sets of recorded human actions, one set containing manner actions and one set containing result actions, and then tested it by letting a human performer carry out the actions online in real time in front of the system's 3D-camera. The system successfully recognized more than 94% of the manner actions and most of the result actions carried out by the human performer.
提出了一种基于层次自组织地图的人类行为在线识别系统。我们对我们的系统进行了第一次评估,方法是在两组不同的人类行为记录上进行训练,一组包含方式行为,另一组包含结果行为,然后通过让人类表演者在系统的3d相机前实时在线执行这些行为来测试它。该系统成功识别了超过94%的方式动作和大多数由人类表演者执行的结果动作。
{"title":"Action Recognition Online with Hierarchical Self-Organizing Maps","authors":"Zahra Gharaee, P. Gärdenfors, Magnus Johnsson","doi":"10.1109/SITIS.2016.91","DOIUrl":"https://doi.org/10.1109/SITIS.2016.91","url":null,"abstract":"We present a hierarchical self-organizing map based system for online recognition of human actions. We have made a first evaluation of our system by training it on two different sets of recorded human actions, one set containing manner actions and one set containing result actions, and then tested it by letting a human performer carry out the actions online in real time in front of the system's 3D-camera. The system successfully recognized more than 94% of the manner actions and most of the result actions carried out by the human performer.","PeriodicalId":403704,"journal":{"name":"2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114269031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 11
期刊
2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)
全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1