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Object recognition supported by user interaction for service robots最新文献

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Why does output normalization create problems in multiple classifier systems? 为什么输出归一化会在多个分类器系统中产生问题?
Pub Date : 2002-12-10 DOI: 10.1109/ICPR.2002.1048417
H. Altınçay, M. Demirekler
A combination of classifiers is a promising direction for obtaining better classification systems. However the outputs of different classifiers may have different scales and hence the classifier outputs are incomparable. Incomparability of the classifier output scores is a major problem in the combination of different classification systems. In order to avoid this problem, the measurement level classifier outputs are generally normalized. However recent studies have proven that output normalization may provide some problems. For instance, the multiple classifier system's performance may become worse than that of a single individual classifier. This paper presents some interesting observations about the reason why such undesirable behavior occurs.
分类器的组合是获得更好的分类系统的一个有前途的方向。然而,不同分类器的输出可能有不同的尺度,因此分类器的输出是不可比较的。分类器输出分数的不可比较性是不同分类系统组合时的一个主要问题。为了避免这个问题,测量级分类器的输出通常被归一化。然而,最近的研究表明,输出归一化可能会带来一些问题。例如,多分类器系统的性能可能会比单个分类器的性能差。本文就这种不良行为发生的原因提出了一些有趣的观察结果。
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引用次数: 22
Tree pruning for output coded ensembles 对输出编码集合进行树修剪
Pub Date : 2002-12-10 DOI: 10.1109/ICPR.2002.1048245
T. Windeatt, G. Ardeshir
Output coding is a method of converting a multiclass problem into several binary subproblems and gives an ensemble of binary classifiers. Like other ensemble methods, its performance depends on the accuracy and diversity of base classifiers. If a decision tree is chosen as base classifier the issue of tree pruning needs to be addressed. In this paper we investigate the effect of six methods of pruning on ensembles of trees generated by error-correcting output code (ECOC). Our results show that error-based pruning outperforms on most datasets but it is better not to prune than to select a single pruning strategy for all datasets.
输出编码是一种将多类问题转化为若干二值子问题的方法,它给出了一组二值分类器。与其他集成方法一样,其性能取决于基分类器的准确性和多样性。如果选择决策树作为基分类器,则需要解决树修剪问题。本文研究了六种剪枝方法对纠错输出码(ECOC)生成的树集的影响。我们的结果表明,基于错误的剪枝在大多数数据集上都表现得更好,但最好不要剪枝,而不是为所有数据集选择单一的剪枝策略。
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引用次数: 5
An appearance based approach for video object extraction and representation 一种基于外观的视频对象提取与表示方法
Pub Date : 2002-12-10 DOI: 10.1109/ICPR.2002.1048358
Gaurav Garg, P. Sharma, S. Chaudhury, R. Choudhury
We describe a novel appearance based scheme for extraction and representation of video objects. The tracking algorithm used for video object extraction is based upon a new eigen-space update scheme. We propose a scheme for organisation of video objects in an appearance based hierarchy. The appearance based hierarchy is constructed using a new SVD based eigen-space merging algorithm. The hierarchy enables approximate query resolution. Experiments performed on a large number of video sequences have yielded promising results.
我们描述了一种新的基于外观的视频对象提取和表示方案。用于视频对象提取的跟踪算法基于一种新的特征空间更新方案。我们提出了一种基于外观层次结构的视频对象组织方案。采用一种新的基于奇异值分解的特征空间合并算法构建基于外观的层次结构。层次结构支持近似的查询解析。在大量的视频序列上进行的实验已经产生了令人鼓舞的结果。
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引用次数: 3
Object detection in images: run-time complexity and parameter selection of support vector machines 图像中的目标检测:支持向量机的运行复杂度和参数选择
Pub Date : 2002-12-10 DOI: 10.1109/ICPR.2002.1048330
N. Ancona, G. Cicirelli, E. Stella, A. Distante
We address two aspects related to the exploitation of support vector machines (SVM) for classification in real application domains, such as the detection of objects in images. The first one concerns the reduction of the run-time complexity of a reference classifier without increasing its generalization error. We show that the complexity in test phase can be reduced by training SVM classifiers on a new set of features obtained by using principal component analysis (PCA). Moreover due to the small number of features involved, we explicitly map the new input space in the feature space induced by the adopted kernel function. Since the classifier is simply a hyperplane in the feature space, then the classification of a new pattern involves only the computation of a dot product between the normal to the hyperplane and the pattern. The second issue concerns the problem of parameter selection. In particular we show that the receiver operating characteristic curves, measured on a suitable validation set, are effective for selecting, among the classifiers the machine implements, the one having performances similar to the reference classifier. We address these two issues for the particular application of detecting goals during a football match.
我们解决了与支持向量机(SVM)在实际应用领域中的分类利用相关的两个方面,例如图像中物体的检测。第一个问题是在不增加泛化误差的情况下降低参考分类器的运行时复杂度。我们证明了使用主成分分析(PCA)获得的一组新的特征来训练SVM分类器可以降低测试阶段的复杂性。此外,由于涉及的特征数量较少,我们显式地将新的输入空间映射到由所采用的核函数引起的特征空间中。由于分类器只是特征空间中的一个超平面,那么新模式的分类只涉及计算超平面的法线与模式之间的点积。第二个问题是参数选择问题。特别是,我们表明,在一个合适的验证集上测量的接收器工作特性曲线,对于在机器实现的分类器中选择具有与参考分类器相似性能的分类器是有效的。我们针对足球比赛中检测目标的特定应用解决了这两个问题。
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引用次数: 13
Probabilistic tracking with optimal scale and orientation selection 具有最佳规模和方向选择的概率跟踪
Pub Date : 2002-12-10 DOI: 10.1109/ICPR.2002.1048390
Hwann-Tzong Chen, Tyng-Luh Liu
We describe a probabilistic framework based on a trust-region method to track rigid or non-rigid objects with automatic optimal scale and orientation selection. The approach uses a flexible probability model to represent an object by its salient features such as color or intensity gradient. Depending on the weighting scheme, features will contribute to the distribution differently according to their positions. We adopt a bivariate normal as the weighting function that only features within the induced covariance ellipse are considered. Notice that characterizing an object by a covariance ellipse makes it easier to define its orientation and scale. To perform tracking, a trust-region scheme is carried out for each image frame to detect a distribution similar to the target's accounting for the translation, scale, and orientation factors simultaneously. Unlike other work, the optimization process is executed over a continuous space. Consequently, our method is more robust and accurate as demonstrated in the experimental results.
我们描述了一种基于信任域方法的概率框架,通过自动选择最优尺度和方向来跟踪刚性或非刚性物体。该方法使用灵活的概率模型,通过物体的显著特征(如颜色或强度梯度)来表示物体。根据权重方案的不同,特征的位置不同,对分布的贡献也不同。我们采用二元正态作为权重函数,只考虑诱导协方差椭圆内的特征。注意,用协方差椭圆来描述一个对象可以更容易地定义它的方向和比例。为了进行跟踪,对每一帧图像执行信任区域方案,同时考虑平移、尺度和方向因素,检测与目标相似的分布。与其他工作不同,优化过程是在连续空间上执行的。实验结果表明,该方法具有更好的鲁棒性和准确性。
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引用次数: 1
Pattern classification using support vector machine ensemble 基于支持向量机集成的模式分类
Pub Date : 2002-12-10 DOI: 10.1109/ICPR.2002.1048262
Hyun-Chul Kim, Shaoning Pang, Hong-Mo Je, Daijin Kim, S. Bang
While the support vector machine (SVM) can provide a good generalization performance, the classification result of the SVM is often far from the theoretically expected level in practical implementation because they are based on approximated algorithms due to the high complexity of time and space. To improve the limited classification performance of the real SVM, we propose to use an SVM ensemble with bagging (bootstrap aggregating) or boosting. In bagging, each individual SVM is trained independently, using randomly chosen training samples via a bootstrap technique. In boosting, each individual SVM is trained using training samples chosen according to the sample's probability distribution, which is updated in proportion to the degree of error of the sample. In both bagging and boosting, the trained individual SVMs are aggregated to make a collective decision in several ways, such as majority voting, least squares estimation based weighting, and double-layer hierarchical combination. Various simulation results for handwritten digit recognition and fraud detection show that the proposed SVM ensemble with bagging or boosting greatly outperforms a single SVM in terms of classification accuracy.
虽然支持向量机(SVM)可以提供良好的泛化性能,但由于时间和空间的高度复杂性,支持向量机基于近似算法,在实际实现中其分类结果往往与理论预期水平相差甚远。为了提高真实支持向量机有限的分类性能,我们建议使用带有bagging (bootstrap aggregating)或boosting的支持向量机集成。在bagging中,每个单独的SVM通过bootstrap技术使用随机选择的训练样本进行独立训练。在boosting中,使用根据样本的概率分布选择的训练样本来训练每个单独的SVM,训练样本与样本的误差程度成比例更新。在bagging和boosting两种方法中,训练过的单个svm通过多数投票、基于最小二乘估计的加权和双层分层组合等方式聚集在一起做出集体决策。手写体数字识别和欺诈检测的各种仿真结果表明,采用bagging或boosting方法的支持向量机集成在分类精度上大大优于单个支持向量机。
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引用次数: 76
Rapid generation of event-based indexes for personalized video digests 为个性化视频摘要快速生成基于事件的索引
Pub Date : 2002-12-10 DOI: 10.1109/ICPR.2002.1048483
M. Teraguchi, Ken Masumitsu, T. Echigo, Shun-ichi Sekiguchi, M. Etoh
This paper presents a novel video indexing method for providing timely personalized video delivery services. Several previous research reports have dealt with automatic content-based indexing. However, these systems require a lot of time to manually correct the indexes after preliminary automatic processing, making it difficult to use these indexes in practical services. In our method, reliable content-based indexes can be automatically generated by manually flagging predefined and easily recognizable events while watching the video (without rewinding). The generated indexes use temporal functions of significance based on some parameters predefined for each kind of flagged event. Therefore, we can reduce the time required for flagging and achieve rapid semi-manual video indexing. Finally, we confirm effectiveness of the indexes transformed into a subset of MPEG-7 descriptions in a practical service to automatically construct personalized video digests.
本文提出了一种新的视频索引方法,以提供及时的个性化视频交付服务。以前的一些研究报告已经处理了基于内容的自动索引。然而,这些系统在初步的自动处理后,需要花费大量的时间来手动校正这些索引,这使得这些索引在实际服务中难以使用。在我们的方法中,可以通过在观看视频时(无需倒带)手动标记预定义且易于识别的事件来自动生成可靠的基于内容的索引。生成的索引使用基于为每种标记事件预定义的一些参数的时间显著性函数。因此,我们可以减少标记所需的时间,实现快速的半人工视频索引。最后,我们在实际服务中验证了将索引转换为MPEG-7描述子集以自动构建个性化视频摘要的有效性。
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引用次数: 5
Use of characteristic views in image classification 特征视图在图像分类中的应用
Pub Date : 2002-12-10 DOI: 10.1109/ICPR.2002.1048462
C. Hung, Shisong Yang, C. Laymon
This paper addresses the problem of image texture classification. We present a novel texture feature called "characteristic view", which is directly extracted from a sample sub-image corresponding to each texture class. The K-views template method is proposed to classify the texture pixels based on these features. The characteristic view concept is based on the assumption that in an image taken from the nature scenes, a specific texture class in this image will frequently reveal the repetitions of some certain classes of features. Different "views" can be obtained for these features from different spatial locations. Experimental results show the effectiveness of the proposed approach compared with other methods.
本文主要研究图像纹理分类问题。我们提出了一种新的纹理特征,称为“特征视图”,它是直接从每个纹理类对应的样本子图像中提取的。基于这些特征,提出了K-views模板方法对纹理像素进行分类。特征视图的概念是基于这样的假设,即在取自自然场景的图像中,该图像中的特定纹理类将经常显示某些特定类别的特征的重复。这些特征在不同的空间位置可以获得不同的“视图”。实验结果表明,该方法与其他方法相比是有效的。
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引用次数: 11
Image segmentation using gradient vector diffusion and region merging 基于梯度矢量扩散和区域合并的图像分割
Pub Date : 2002-12-10 DOI: 10.1109/ICPR.2002.1048460
Zeyun Yu, C. Bajaj
The Active Contour (or Snake) Model is recognized as one of the efficient tools for 2D/3D image segmentation. However traditional snake models prove to be limited in several aspects. The present paper describes a set of diffusion equations applied to image gradient vectors, yielding a vector field over the image domain. The obtained vector field provides the Snake Model with an external force as well as an automatic way to generate the initial contours. Finally a region merging technique is employed to further improve the segmentation results.
活动轮廓(Snake)模型被认为是二维/三维图像分割的有效工具之一。然而,传统的蛇模型在几个方面被证明是有限的。本文描述了一组应用于图像梯度矢量的扩散方程,得到了图像域上的矢量场。得到的矢量场为Snake模型提供了一个外力,同时也提供了一种自动生成初始轮廓的方法。最后采用区域合并技术进一步提高分割效果。
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引用次数: 49
Textual description of human activities by tracking head and hand motions 通过跟踪头部和手部的运动来对人类活动进行文字描述
Pub Date : 2002-12-10 DOI: 10.1109/ICPR.2002.1048491
A. Kojima, Takeshi Tamura, K. Fukunaga
We propose a method for describing human activities from video images by tracking human skin regions: facial and hand regions. To detect skin regions robustly, three kinds of probabilistic information are extracted and integrated using Dempster-Shafer theory. The main difficulty in transforming video images into textual descriptions is bridging the semantic gap between them. By associating visual features of head and hand motion with natural language concepts, appropriate syntactic components such as verbs, objects, etc. are determined and translated into natural language.
我们提出了一种通过跟踪人体皮肤区域(面部和手部)来描述视频图像中人类活动的方法。为了对皮肤区域进行鲁棒检测,利用Dempster-Shafer理论提取和整合了三种概率信息。将视频图像转换为文本描述的主要困难是弥合它们之间的语义差距。通过将头部和手部运动的视觉特征与自然语言概念联系起来,确定适当的句法成分,如动词、宾语等,并将其翻译成自然语言。
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引用次数: 20
期刊
Object recognition supported by user interaction for service robots
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