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Multi-Scale Spatial Concatenations of Local Features in Natural Scenes and Scene Classification

机译:自然场景和场景分类中局部特征的多尺度空间级联

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摘要

How does the visual system encode natural scenes? What are the basic structures of natural scenes? In current models of scene perception, there are two broad feature representations, global and local representations. Both representations are useful and have some successes; however, many observations on human scene perception seem to point to an intermediate-level representation.In this paper, we proposed natural scene structures, i.e., multi-scale spatial concatenations of local features, as an intermediate-level representation of natural scenes. To compile the natural scene structures, we first sampled a large number of multi-scale circular scene patches in a hexagonal configuration. We then performed independent component analysis on the patches and classified the independent components into a set of clusters using the K-means method. Finally, we obtained a set of natural scene structures, each of which is characterized by a set of dominant clusters of independent components.We examined a range of statistics of the natural scene structures, compiled from two widely used datasets of natural scenes, and modeled their spatial arrangements at larger spatial scales using adjacency matrices. We found that the natural scene structures include a full range of concatenations of visual features in natural scenes, and can be used to encode spatial information at various scales. We then selected a set of natural scene structures with high information, and used the occurring frequencies and the eigenvalues of the adjacency matrices to classify scenes in the datasets. We found that the performance of this model is comparable to or better than the state-of-the-art models on the two datasets. These results suggest that the natural scene structures are a useful intermediate-level representation of visual scenes for our understanding of natural scene perception.
机译:视觉系统如何编码自然场景?自然场景的基本结构是什么?在当前的场景感知模型中,有两种广泛的特征表示,即全局表示和局部表示。两种表示都是有用的,并且取得了一些成功。然而,关于人类场景感知的许多观察似乎指向中间层表示。在本文中,我们提出了自然场景结构,即局部特征的多尺度空间级联,作为自然场景的中间层表示。为了编译自然场景结构,我们首先以六边形配置对大量的多尺度圆形场景补丁进行了采样。然后,我们对补丁进行了独立的成分分析,并使用K-means方法将独立的成分分类为一组聚类。最后,我们获得了一组自然场景结构,每个特征都由一组独立成分的显性簇组成。我们检查了自然场景结构的统计范围,并从两个广泛使用的自然场景数据集中进行了建模使用邻接矩阵在更大的空间尺度上进行空间布局。我们发现自然场景结构包括自然场景中视觉特征的完整范围,并且可以用来编码各种尺度的空间信息。然后,我们选择了一组具有较高信息的自然场景结构,并使用了发生频率和邻接矩阵的特征值对数据集中的场景进行分类。我们发现,该模型的性能与两个数据集上的最新模型相当或更高。这些结果表明,自然景物结构是视觉景物的有用的中间层表示形式,有助于我们对自然景物感知的理解。

著录项

  • 期刊名称 other
  • 作者

    Xiaoyuan Zhu; Zhiyong Yang;

  • 作者单位
  • 年(卷),期 -1(8),9
  • 年度 -1
  • 页码 e76393
  • 总页数 16
  • 原文格式 PDF
  • 正文语种
  • 中图分类
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