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首页> 外文期刊>Optical engineering >Spatial dependence matrix feature and redundancy elimination algorithm using Ada Boost for object detection
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Spatial dependence matrix feature and redundancy elimination algorithm using Ada Boost for object detection

机译：使用Ada Boost进行目标检测的空间依赖矩阵特征和冗余消除算法

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摘要

This paper describes a novel feature representation and se-lection approach for classification problems, especially for visual object detection within the framework of AdaBoost. This work is distinguished by two contributions. The first contribution is the introduction of a new feature generation and representation method called the spatial depen-dence matrix feature, which not only provides information related to the first-order statistics distribution of the object, but also gives some informa-tion about the relative positions within the object, more importantly, it can provide different degrees of importance for different discriminative parts within the object. It is flexible, extendable, and compatible with Haar-like features. The second contribution is an improved feature selection algo-rithm, which introduces a novel weighted features redundancy elimination rule that eliminates the irrelevant and redundant features from the candi-date feature pool at every boosting stage when gradually training detector, and thus two advantages can be obtained: leading to selecting features with more discrimination and the final detector having a higher accuracy, and also increasing the learning convergence and achieving high train-ing rates. Extensive experiments with synthetic and real scene data sets show that our works outperform conventional AdaBoost and are better than or at least equivalent to previously published results.
机译：本文介绍了一种新颖的特征表示和选择方法，用于分类问题，尤其是在AdaBoost框架内的视觉对象检测。这项工作有两个贡献。第一个贡献是引入了一种称为空间依赖矩阵特征的新特征生成和表示方法，该方法不仅提供与对象的一阶统计分布有关的信息，而且还提供了有关相对信息的信息。物体内的位置，更重要的是，它可以为物体内的不同区分部分提供不同程度的重要性。它具有灵活性，可扩展性，并且与类似Haar的功能兼容。第二个贡献是改进的特征选择算法，它引入了一种新颖的加权特征冗余消除规则，该规则在逐步训练检测器时的每个提升阶段从候选特征池中消除了无关和冗余特征，因此有两个优点获得：导致选择具有更多辨别力的特征，并且最终的检测器具有更高的准确度，并且还提高了学习收敛性并实现了高训练率。使用合成和真实场景数据集进行的大量实验表明，我们的作品优于传统的AdaBoost，并且优于或至少等同于先前发表的结果。

著录项

  • 来源
    《Optical engineering》 |2011年第5期|p.057202.1-057202.16|共16页
  • 作者

    Jia Wen; Chao Li; Zhang Xiong;

  • 作者单位

    Beihang University School of Computer Science and Engineering XueYuan Road No. 37, HaiDian District Beijing 100191, China Shenzhen Key Laboratory of Data Vitalization (Smart City) VU Park, High-tech Industrial Estate Nashan District, Shenzhen 518000, China Yanshan University College of Information Science and Engineering Department of Computer 438, Hebei Avenue Qinhuangdao 066004, China;

    Beihang University School of Computer Science and Engineering XueYuan Road No. 37, HaiDian District Beijing 100191, China Shenzhen Key Laboratory of Data Vitalization (Smart City) VU Park, High-tech Industrial Estate Nashan District, Shenzhen 518000, China;

    Beihang University School of Computer Science and Engineering XueYuan Road No. 37, HaiDian District Beijing 100191, China Shenzhen Key Laboratory of Data Vitalization (Smart City) VU Park, High-tech Industrial Estate Nashan District, Shenzhen 518000, China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    feature representation; feature selection; conditional mutual information; adaboost; object detection;

    机译：特征表示;特征选择;有条件的相互信息;adaboost;目标检测;

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