Spatial Coordinate Coding to reduce histogram representations, Dominant Angle and Colour Pyramid Match
Spatial Coordinate Coding to reduce histogram representations, Dominant Angle and Colour Pyramid Match
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DOI：
10.1109/icip.2011.6116639
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发表时间：
2011-12
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影响因子：
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通讯作者：
Piotr Koniusz;K. Mikolajczyk
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文献类型：
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作者：
Piotr Koniusz;K. Mikolajczyk
空间金字塔匹配是现代物体类别识别系统的核心。一旦图像描述符被表示为视觉词的直方图，它们就被进一步部署在具有由粗到细的空间位置网格的空间金字塔上。然而，这样的表示导致200K或更多元素的极端直方图向量增加计算和存储器需求。本文研究了在直方图形成过程中引入空间信息的替代方法。具体来说，我们建议在描述符级别应用空间位置信息，并将其称为空间坐标编码。或者，使用x、y、半径或角度来执行半译码。这是通过在描述符级别添加一个空间分量，同时将金字塔匹配应用于另一个空间分量来实现的。最后，我们证明了金字塔匹配可以稳健地应用于其他测量：主导角度和颜色。我们展示了两个数据集的最先进的结果与软分配和稀疏编码的手段。
Spatial Pyramid Match lies at a heart of modern object category recognition systems. Once image descriptors are expressed as histograms of visual words, they are further deployed across spatial pyramid with coarse-to-fine spatial location grids. However, such representation results in extreme histogram vectors of 200K or more elements increasing computational and memory requirements. This paper investigates alternative ways of introducing spatial information during formation of histograms. Specifically, we propose to apply spatial location information at a descriptor level and refer to it as Spatial Coordinate Coding. Alternatively, x, y, radius, or angle is used to perform semi-coding. This is achieved by adding one of the spatial components at the descriptor level whilst applying Pyramid Match to another. Lastly, we demonstrate that Pyramid Match can be applied robustly to other measurements: Dominant Angle and Colour. We demonstrate state-of-the art results on two datasets with means of Soft Assignment and Sparse Coding.