结合显著性的主动轮廓图像分割

传统的主动轮廓方法无法突出分割区域的显著性，同时在由显著性检测算法所得到的显著图中目标具有较高的信噪比，因此提出结合显著性的主动轮廓图像分割。通过线性光谱聚类分割得到超像素，以超像素为处理单位利用基于图论的流形排序算法获得较好的显著图；将高斯混合模型引入到主动轮廓的曲线演化过程中，计算曲线内外的平均灰度值，从而通过高斯混合模型和显著性信息得到了新的主动轮廓能量方程，并运用水平集方法指导分割，获得最终的分割结果。实验结果表明，提出的图像分割方法可以对图像进行快速和有效的分割。

Active Contour Image Segmentation Combined with Saliency

The traditional active contour method cannot highlight the saliency of the segmented region. At the same time, the target in the saliency map obtained by the saliency detection algorithm has a higher SNR. This paper proposes an active contour image segmentation combining saliency. First, superpixels are obtained by linear spectral clustering segmentation. Superpixels are used as processing units to obtain better saliency maps based on a graph theory-based manifold ranking algorithm. Then, the Gaussian mixture model is introduced into the curve evolution process of the active contour, and the average gray value inside and outside the curve is calculated. Thus, a new active contour energy equation is obtained through the Gaussian mixture model and saliency information, and the level set method is used to guide the segmentation. The final segmentation result is obtained. Experimental results show that the image segmentation method proposed in this paper can segment images quickly and efficiently.