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首页> 外文期刊>Journal of King Saud University >Ensemble of different local descriptors, codebook generation methods and subwindow configurations for building a reliable computer vision system
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Ensemble of different local descriptors, codebook generation methods and subwindow configurations for building a reliable computer vision system

机译：集成了不同的本地描述符，代码簿生成方法和子窗口配置，以构建可靠的计算机视觉系统

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In the last few years, several ensemble approaches have been proposed for building high performance systems for computer vision. In this paper we propose a system that incorporates several perturbation approaches and descriptors for a generic computer vision system. Some of the approaches we investigate include using different global and bag-of-feature-based descriptors, different clusterings for codebook creations, and different subspace projections for reducing the dimensionality of the descriptors extracted from each region. The basic classifier used in our ensembles is the Support Vector Machine. The ensemble decisions are combined by sum rule. The robustness of our generic system is tested across several domains using popular benchmark datasets in object classification, scene recognition, and building recognition. Of particular interest are tests using the new VOC2012 database where we obtain an average precision of 88.7 (we submitted a simplified version of our system to the person classification-object contest to compare our approach with the true state-of-the-art in 2012). Our experimental section shows that we have succeeded in obtaining our goal of a high performing generic object classification system. The MATLAB code of our system will be publicly available at http://www.dei.unipd.it/wdyn/?IDsezione=3314&IDgruppo_pass=124&preview= . Our free MATLAB toolbox can be used to verify the results of our system. We also hope that our toolbox will serve as the foundation for further explorations by other researchers in the computer vision field.
机译：在最近几年中，已经提出了几种集成方法来构建用于计算机视觉的高性能系统。在本文中，我们提出了一种系统，该系统结合了通用计算机视觉系统的几种摄动方法和描述符。我们研究的一些方法包括使用不同的全局和基于特征包的描述符，用于代码簿创建的不同聚类以及用于减少从每个区域提取的描述符的维数的不同子空间投影。我们的合奏中使用的基本分类器是支持向量机。合计决策由求和规则合并。我们的通用系统的鲁棒性已在对象分类，场景识别和建筑物识别中使用流行的基准数据集在多个域中进行了测试。特别令人感兴趣的是使用新的VOC2012数据库进行的测试，我们获得了88.7的平均精确度（我们将系统的简化版本提交给了人员分类对象竞赛，以将我们的方法与2012年的真实水平进行比较）。我们的实验部分表明，我们已经成功实现了高性能通用对象分类系统的目标。我们系统的MATLAB代码将在http://www.dei.unipd.it/wdyn/?IDsezione=3314&IDgruppo_pass=124&preview=上公开提供。我们免费的MATLAB工具箱可用于验证系统结果。我们也希望我们的工具箱将成为计算机视觉领域其他研究人员进一步探索的基础。

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