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Quaternion Harmonic moments and extreme learning machine for color object recognition

机译：四元数谐波矩和极限学习机，用于彩色物体识别

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摘要

The quaternary orthogonal moments have been widely used as color image descriptors owe to their remarkable color and shape information encapsulation capability. Their computation, however, depends on finding the optimal value of a unit pure quaternion parameter, which is done empirically and with no warranty of optimality. We propose a 2D color object recognition method that relies on the quaternion-valued parameter-free disc-harmonic moment invariants (QHMs) fed into the quaternion extreme learning machine (QELM). The role of this latter is to maintain the correlation between the four parts, real and imaginary, of the quaternary descriptor coefficients. Several datasets are used for recognition experiments. We draw the conclusion that: (1) our quaternion-valued QHMs invariants outperform other quaternary moments, (2) the quaternion-valued moment invariants give results better than the modulus-based moment invariants and (3) the QELM yields results better than the state-of-the-art classifiers.
机译：由于其显着的颜色和形状信息封装能力，四元正交矩已被广泛用作彩色图像描述符。但是，它们的计算取决于找到单位纯四元数参数的最优值，这是凭经验完成的，并且不保证最优性。我们提出了一种二维颜色目标识别方法，该方法依赖于输入到四元数极限学习机（QELM）中的四元数值无参数的盘谐矩不变式（QHM）。后者的作用是保持四元描述符系数的四个部分（实部和虚部）之间的相关性。几个数据集用于识别实验。我们得出以下结论：（1）我们的四元数值不变矩优于其他四元矩；（2）四元数值不变矩给出的结果要好于基于模量的矩不变量；（3）QELM产生的结果要好于基于四阶矩的不变式。最新的分类器。

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