Mahalanobis Encodings for Visual Categorization
Mahalanobis Encodings for Visual Categorization
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用于视觉分类的马哈拉诺比斯编码
DOI：
10.2197/ipsjtcva.7.69
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Tsuyoshi Kato
中科院分区：
文献类型：
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作者：
Tomoki Matsuzawa;Raissa Relator;Wataru Takei;S. Omachi;Tsuyoshi Kato
目前，图像表征的设计是视觉分类表现的关键因素之一。在最近的大多数研究中，用于获得图像表示的常见流水线由两个步骤组成：编码步骤和池化步骤。在本文中，我们引入了马氏度量的两个流行的图像块编码模块，直方图编码和Fisher编码，分别用于视觉字袋方法和Fisher矢量方法。此外，对于所提出的Fisher向量方法，可以使用与原始Fisher向量中使用的相同假设来导出Fisher向量的闭合形式近似，并且在不诉诸耗时的EM（期望最大化）步骤的情况下构建码本。多类分类的实验结果表明了该编码方法的有效性。
Nowadays, the design of the representation of images is one of the most crucial factors in the performance of visual categorization. A common pipeline employed in most of recent researches for obtaining an image representa- tion consists of two steps: the encoding step and the pooling step. In this paper, we introduce the Mahalanobis metric to the two popular image patch encoding modules, Histogram Encoding and Fisher Encoding, that are used for Bag- of-Visual-Word method and Fisher Vector method, respectively. Moreover, for the proposed Fisher Vector method, a close-form approximation of Fisher Vector can be derived with the same assumption used in the original Fisher Vector, and the codebook is built without resorting to time-consuming EM (Expectation-Maximization) steps. Experimental evaluation of multi-class classification demonstrates the effectiveness of the proposed encoding methods.
DOI：
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发表时间：
2002
期刊：
2020 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD)
影响因子：
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作者：
Gabriella Csurka;C. Dance;Lixin Fan;J. Willamowski;Cédric Bray
通讯作者：
Gabriella Csurka;C. Dance;Lixin Fan;J. Willamowski;Cédric Bray