There exist large numbers of methods/algorithms which can be used for the classification of aerobic images. While the current method is used to classify the aerobics image, it cannot effectively remove the noise in the aerobics image. The classification time is long, and there are problems of poor denoising effect and low classification efficiency. Therefore, the aerobics image classification algorithm based on the modal symmetry algorithm is proposed. The method of nonlocal mean filtering based on structural features is used to denoise the aerobics image, and the pyramid structure of the image is introduced to decompose the aerobics image. According to the denoising and decomposition results, the enhancement of aerobics image is realized by the logarithmic image processing (LIP) model and gradient sharpening method. Finally, the aerobics image after the enhancement is classified by a modal symmetry algorithm. Experimental results show that the proposed method has a good denoising effect and high classification efficiency, which shows that the algorithm has significant effectiveness and high application performance.

中文翻译：

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基于模态对称算法的健美操图像分类算法

存在大量可用于有氧图像分类的方法/算法。虽然目前的方法用于对健美操图像进行分类，但无法有效去除健美操图像中的噪声。分类时间长，存在去噪效果差、分类效率低等问题。因此，提出了基于模态对称算法的健美操图像分类算法。采用基于结构特征的非局部均值滤波方法对健美操图像进行去噪，并引入图像的金字塔结构对健美操图像进行分解。根据去噪和分解结果，通过对数图像处理（LIP）模型和梯度锐化方法实现有氧运动图像的增强。最后，增强后的有氧运动图像通过模态对称算法进行分类。实验结果表明，该方法具有良好的去噪效果和较高的分类效率，表明该算法具有显着的有效性和较高的应用性能。