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Adaptive Multilevel Kernel Machine for Scene Classification

机译：场景分类的自适应多级内核机

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摘要

Scene classification is a challenging problem in computer vision applications and can be used to model and analyze a special complex system, the internet community. The spatial PACT (Principal component Analysis of Census Transform histograms) is a promising representation for recognizing instances and categories of scenes. However, since the original spatial PACT only simply concatenates compact census transform histograms at all levels together, all levels have the same contribution, which ignores the difference among various levels. In order to ameliorate this point, we propose an adaptive multilevel kernel machine method for scene classification. Firstly, it computes a set of basic kernels at each level. Secondly, an effective adaptive weight learning scheme is employed to find the optimal weights for best fusing all these base kernels. Finally, support vector machine with the optimal kernel is used for scene classification. Experiments on two popular benchmark datasets demonstrate that the proposed adaptive multilevel kernel machine method outperforms the original spatial PACT. Moreover, the proposed method is simple and easy to implement.
机译：场景分类是计算机视觉应用程序中一个具有挑战性的问题，可用于建模和分析特殊的复杂系统，即Internet社区。空间PACT（人口普查变换直方图的主成分分析）是一种有前景的识别场景实例和类别的表示。但是，由于原始空间PACT仅简单地将所有级别的紧凑型普查变换直方图连接在一起，因此所有级别都有相同的贡献，而忽略了各个级别之间的差异。为了改善这一点，我们提出了一种用于场景分类的自适应多级内核机方法。首先，它在每个级别上计算一组基本内核。其次，采用有效的自适应权重学习方案来找到最优权重，以最佳融合所有这些基本内核。最后，将具有最优内核的支持向量机用于场景分类。在两个流行的基准数据集上进行的实验表明，所提出的自适应多级核机方法优于原始空间PACT。此外，所提出的方法简单且易于实现。

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  • 来源
    《Mathematical Problems in Engineering》 |2013年第3期|324945.1-324945.9|共9页
  • 作者

    Junlin Hu; Liang Wang; Fuqing Duan; Ping Guo;

  • 作者单位

    Image Processing and Pattern Recognition Laboratory, Beijing Normal University, Beijing 100875, China;

    College of Electronic Information and Control Engineering, Beijing University of Technology, Beijing 100124, China;

    College of Information Science and Technology, Beijing Normal University, Beijing 100875, China;

    Image Processing and Pattern Recognition Laboratory, Beijing Normal University, Beijing 100875, China;

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