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中文摘要：
针对均值漂移算法收敛速度较慢的问题，本文提出了基于共轭梯度的快速均值漂移算法，并将其用于图像分割。该算法利用共轭梯度法简便，存储需求小，收敛速度介于最速下降法和牛顿法之间，具有较好的全局收敛性和较快的收敛速度的特点，通过交替执行均值漂移算法和共轭梯度算法提高经典均值漂移算法的收敛速度。对合成图像和真实图像的实验结果表明了新算法不但提高了经典均值漂移算法的速度，而且在进行图像分割时保持了良好的分割结果。
英文摘要：
Since the convergence velocity of mean shift is too slow, fast mean shift for image segmentation is proposed based on conjugate gradient. Conjugate gradient method is characterized by simple, low memory requirements and local and global convergence properties. Moreover, the convergence velocity of conjugate gradient method is between steepest descent method and Newton method. The new algorithm makes use of the properties of conjugate gradient method to improve the convergence velocity of traditional mean shift by interleaved execution of mean shift and conjugate gradient method. Experimental results on synthetic and real images show that new algorithm not only improves the velocity of classical mean shift, but also keeps better segmented result in image segmentation.
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