Galaxies’ studies play an important role in the astronomic. Accurate classification of these galaxies enables scientists to understand the formation and evolution of the Universe. During the last decades, there have been several methods applied to classify the galaxy images. However, these methods encounter three big challenges. First, most existing methods converted the color images of galaxies into gray images which result in losing the essential color information. Second, the utilized feature selection methods, that used to remove the irrelevant features, may be stuck at the attractive local point. Third, using an irrelevant classifier could lead to decrease the classification accuracy. In this paper, a new algorithm is proposed to classify color images of galaxies. In this algorithm, highly accurate non-redundant color features are extracted from the color images of galaxies by using the quaternion polar complex exponential transform moments (QPCET). The quaternion representation deals with a color image in a holistic way which keeps the correlation between components and then successfully represents the color images. The QPCET moments are highly accurate, noise resistant, and numerically stable. Moreover, these moments are invariants with respect to rotation, scaling and translation (RST). These characteristics assure the excellency of the extracted color features. The Stochastic Fractal Search (SFS) has a very high ability to avoid the stuck at local point. Its binary version is utilized to select the most appropriate features which improve the classification process. The Extreme Machine learning (EML) is used to classify the color images of galaxies using the selected color features. Experiments are performed with the well-known datasets of galaxies (EFIGI catalog), where the proposed algorithm achieved high classification rate. The obtained results clearly show that the proposed method outperformed all existing galaxies classification methods.

星系的研究在天文学中起着重要作用。这些星系的准确分类使科学家能够了解宇宙的形成和演化。在过去的几十年中，已经应用了几种方法对银河图像进行分类。但是，这些方法面临三个大挑战。首先，大多数现有方法将星系的彩色图像转换为灰色图像，从而导致丢失必要的颜色信息。第二，用于去除不相关特征的利用特征选择方法可能会停留在吸引人的局部点。第三，使用不相关的分类器可能会导致分类准确性降低。本文提出了一种新的星系彩色图像分类算法。在这种算法中 通过使用四元数极性复数指数变换矩（QPCET）从星系的彩色图像中提取高度准确的非冗余颜色特征。四元数表示以整体方式处理彩色图像，该方式保持组件之间的相关性，然后成功表示彩色图像。QPCET力矩非常准确，抗噪声并且数值稳定。此外，这些力矩相对于旋转，缩放和平移（RST）是不变的。这些特性确保了所提取色彩特征的出色表现。随机分形搜索（SFS）具有很高的避免卡在本地点的能力。它的二进制版本用于选​​择最合适的功能，从而改善分类过程。极限机器学习（EML）用于使用选定的颜色特征对星系的彩色图像进行分类。使用著名的星系数据集（EFIGI目录）进行了实验，其中所提出的算法获得了很高的分类率。获得的结果清楚地表明，该方法优于所有现有的星系分类方法。