A watershed segmentation algorithm based on adaptive gradient reconstruction is proposed to address over-segmentation in the gray-scale watershed algorithm and to simplify its application on color images. Principal component analysis is used to reduce the dimensionality of color images. The gradient of low-dimensional images is calculated. The gradient image is modified by applying an adaptive gradient reconstruction algorithm. Watershed transformation is employed to the optimized gradient image to achieve color image segmentation. Use performance indicators and the numbers of segmented regions that contain color distance

mean square error and region information to evaluate the segmented results. For different types of color images.This algorithm can correctly segment different types of color images. Compared with the existing watershed segmentation algorithms

the proposed method can effectively eliminate the pseudo minimum caused by irregular details and noise. It can also overcome over-segmentation

thereby improving segmentation accuracy. The proposed algorithm has good applicability and robustness.