Neutrosophy studies the origin, nature, scope of neutralities, and their interactions with different ideational spectra. It is an alternative to the existing logics and represents mathematical uncertainty, vagueness, contradiction, and imprecision. Neutrosophy introduces a new concept to represent indeterminacy, which is a new philosophy to extend fuzzy logic. We introduce neutrosophy to color image segmentation and develop a novel unsupervised algorithm. We determine the centers of image clusters by using color information in red, green, and blue (RGB) color space and define neutrosophic indeterminacy by using spatial information in CIE (L*u*v*) color space. By applying neutrosophy, we integrate color and spatial information, as well as global and local information in two color spaces: RGB and CIE, respectively. Most existing segmentation algorithms have the over-segmentation problem, which causes segmentation accuracy to be lower, especially when noisy images are processed. Neutrosophy has noise-tolerant ability. In the experiment section, USC-SIPI image database is used to compare the proposed algorithm with several fuzzy and non-fuzzy color segmentation algorithms. The experiments demonstrate that the proposed algorithm can handle the segmentation tasks well and can even process noisy images with better accuracy. They also show its noise-tolerant ability. The proposed approach is more effective and powerful for color image segmentation, which can find wide applications in related areas.