School of Computer and Information Technology, Liaoning Normal University, Dalian 116029, China.
Neural Netw. 2012 Sep;33:148-59. doi: 10.1016/j.neunet.2012.04.012. Epub 2012 May 11.
Image segmentation is an important tool in image processing and can serve as an efficient front end to sophisticated algorithms and thereby simplify subsequent processing. In this paper, we present a pixel-based color image segmentation using Support Vector Machine (SVM) and Fuzzy C-Means (FCM). Firstly, the pixel-level color feature and texture feature of the image, which is used as input of the SVM model (classifier), are extracted via the local spatial similarity measure model and Steerable filter. Then, the SVM model (classifier) is trained by using FCM with the extracted pixel-level features. Finally, the color image is segmented with the trained SVM model (classifier). This image segmentation can not only take full advantage of the local information of the color image but also the ability of the SVM classifier. Experimental evidence shows that the proposed method has a very effective computational behavior and effectiveness, and decreases the time and increases the quality of color image segmentation in comparison with the state-of-the-art segmentation methods recently proposed in the literature.
图像分割是图像处理中的一个重要工具，可以作为复杂算法的有效前端，从而简化后续处理。在本文中，我们提出了一种基于像素的彩色图像分割方法，该方法使用支持向量机（SVM）和模糊 C 均值（FCM）。首先，通过局部空间相似性度量模型和可操纵滤波器提取图像的像素级颜色特征和纹理特征，作为 SVM 模型（分类器）的输入。然后，使用提取的像素级特征通过 FCM 对 SVM 模型（分类器）进行训练。最后，使用训练好的 SVM 模型（分类器）对彩色图像进行分割。这种图像分割不仅可以充分利用彩色图像的局部信息，还可以利用 SVM 分类器的能力。实验证据表明，与文献中最近提出的最新分割方法相比，该方法具有非常有效的计算行为和有效性，并且减少了时间并提高了彩色图像分割的质量。
